Collaboration is Dominating Quantum Computing

Nearly every day we see headlines about players in the Quantum Computing industry announcing new collaborations and partnerships.  On a technical level this seems logical given the unique requirements of the various components such as the physical qubits, cryogenics, lasers, microwaves, controllers, compilers, error correction, various software layers, etc.  And remember, we are talking about tiny, tiny size and time scales where things are measured in microseconds or shorter, and the tiniest imperfections or noise can throw off an entire calculation.  Operating at the cutting edge of technology, many of these components require intense specialization, so it makes sense it would be difficult for one company to have the required expertise and resources to create best-in-class solutions for all these disparate components.  Collaborations would seem inevitable as the industry attempts to power forward as rapidly as possible.  In fact, as Paul Lipman, President of Quantum Information Platforms at ColdQuanta recently told me, “to really have the kind of multi-trillion dollar impact on the economy that we all believe quantum computing could represent…we only get there through really deep collaboration.”  And while the focus on this post is on commercial businesses, as Paul further noted “it’s going to require innovation on a fairly broad basis.  Not just collaborations between companies, but collaborations between governments and companies, between nonprofit organizations, academia, and others…collaborations in all directions.” 

Recently, Xanadu and NVIDIA announced a collaboration to provide native GPU support and high-performance computing (HPC) capabilities to Xanadu’s open-source PennyLane software.  Pasqal, the atom-based quantum hardware business announced a merger with Qu&Co., a quantum algorithm and software developer and Quantum Machines, a QC cloud infrastructure company is acquiring QDevil, a quantum hardware and auxiliary electronics firm.  Quantum Computing Inc (QCI) announced a marketing agreement with QPhoton, Inc. to combine QCI’s Qatalyst software with QPhoton’s photonic quantum technologies.  Q-CTRL has a collaboration with Quantum Machines, IBM has a partnership with Quantinuum, and Rigetti, Deloitte and Strangeworks have a three-way collaboration.  In fact, Strangeworks has collaborations with seemingly everyone in the Quantum Computing (QC) space.  There are dozens and dozens of additional examples.

Rather than try to describe all the many QC collaborations, I wanted to drill down on a few, partly to help emphasize the nature and benefits of such arrangements, and to introduce readers to a few companies moving fast and making important advances in Quantum Computing. 

The following graphic gives a sampling of the inter-connected web being built in the industry:

Before I describe some of these partnerships and collaborations, I want to provide further details on some of the featured players noted, so the following are brief descriptions of Strangeworks, ColdQuanta and Classiq.  I am also drafting deeper dives on each of these companies which I’ll post as separate blogs in coming weeks.

Strangeworks, Inc.

Headquartered in Austin, Texas, Strangeworks is comprised of experienced serial entrepreneurs, enterprise software developers, and quantum physicists with an aim ofmaking QC accessible to everyone and helping accelerate the integration of QC in corporations, universities, and government agencies.  Strangeworks was founded by its CEO William Hurley, affectionately known as “whurley.”  Whurley has quite the resume including being co-founder and general partner at venture firm Ecliptic Capital, a former managing director at Goldman Sachs and he began his career as a R&D Engineer at Apple.  In between he founded a number of companies including Chaotic Moon Studios (software, mobile development and design studio) which was acquired by Accenture and Honest Dollar (a retirement savings plan provider) which was acquired by Goldman.  He is also an Eisenhower Fellow, the first Ambassador to CERN and Society, and co-founder of EQUALS (the Global Partnership for Gender Equality in the Digital Age), among other activities and achievements. 

He admitted to me that the current Strangeworks website doesn’t do a great job at conveying its business model, which is partly by design as the team builds out the platform and ecosystem.  Currently it is targeted at technical users but a web redesign is underway which should expand the target audience.  Presently, there are a few ecosystems on the website including the following:

  • Strangeworks QCTM: A platform used by thousands of researchers, developers, and companies around the world to learn, teach, create, and collaborate on QC projects.  It is hardware agnostic, software-inclusive and extremely collaborative.  Per the website, it has “all of the quantum tools you’ll ever need, delivered in a single user interface.  It is free to use (within certain project size limitations).
  • Strangeworks EQTM: The enterprise version of Strangeworks QC with unlimited project size and added features and customizations.
  • Strangeworks QSTM (Quantum Syndicate): A compendium of hardware, software, education, and cloud service resources and providers.
  • Backstage Pass: Strangeworks has attracted tens of thousands of software developers and programmers from around the world, enabling users to test-drive pre-release features and functionality in exchange for feedback.  While anyone can apply to participate, it is targeted at quantum researchers and developers.  It’s a great win-win arrangement where users get access to the latest and greatest tools and Strangeworks is then able to offer feedback to QC companies to help them improve their systems and programs.

In addition to the programs and features noted above, Strangeworks also acts to help its consultancy and global governmental agency clients behind the scenes, enabling “white label” platforms that users can leverage to enhance their QC capabilities and educational materials for themselves and their own customers and collaborators. 

Given whurley’s front-row seat to nearly the entire QC community, I asked him what sorts of problems QC’s might solve.  He responded that“I don’t believe we should be focused on applying quantum computing to the problems of today…we should be focused on what happens post-quantum and the trillions of dollars of opportunity.  And it’s not just computing, it’s sensors and communications too.  I see trillions and trillions of dollars of opportunities nobody can even fathom today because they’re going to come out of mistakes made on quantum computing experiments or experiments done by unlikely people.  And that’s why it’s important to me to distribute this technology as wide and far and freely as possible.”  Given Strangeworks’ large and growing ecosystems and vast numbers of partners and collaborators, it was a natural company to feature in this post, and given its exciting and evolving model, I look forward to providing additional details and color in a future post.


Based in Boulder, Colorado, ColdQuanta traces its roots to Drs. Eric Cornell and Carl Weiman who created the first ever Bose-Einstein Condensate (BEC) at UC Boulder in 1995, a feat for which they were awarded a 2001 Nobel Prize.  BEC is a new form of matter, which is created when atoms are cooled close to absolute zero.  ColdQuanta uses lasers to arrange either cesium atoms (cooled to a few microKelvin, or millionths of a degree for qubits) or rubidium atoms (cooled to nanoKelvin or billionths of a degree to make BEC, where the atoms act as a single quantum object and are used most notably in sensing) and hold them in place.  Since temperate is a measure of kinetic movement, locking these atoms in place reduces their movement and hence, reduces their temperature.  ColdQuanta uses this cold atom method across multiple quantum applications including gate-based quantum computers as well as a variety of quantum sensing and signal processing applications such as High Precision Clocks, Quantum Positioning Systems (QPS), Quantum Radio Frequency Receivers (QRF) and Quantum Networking and Communications.  While Quantum Computing steals most of the “quantum” headlines these days, these other quantum-enabled devices bring enormous advances in their fields. 

ColdQuanta is on target to release its 100-qubit cloud-based quantum computer called Hilbert, sometime this year.  Hilbert promises superior error correction, high qubit connectivity (4:1), long coherence times, and high gate fidelity, among other features.  And most importantly, despite the super-cold atoms, the device itself operates at room temperature.  In addition, because atoms do not have an electrical charge, they can be packed close together making this method of qubit construction highly scalable and compact.  In fact, there is a ColdQuanta sensing device operating on the International Space Station, and while that is not a Quantum Computer, it reinforces ColdQuanta’s ability to delivery highly stable and compact devices even in the most extreme environments.

ColdQuanta has entered a number of collaborations which I’ll highlight below.  Also, the breadth and depth of ColdQuanta’s capabilities and its unique method of qubit creation, are quite fascinating, as are some of its customers which include NASA and DARPA, so look to this blog for an upcoming feature on ColdQuanta where I will describe more of these details.


Classiq is a quantum algorithm design company based in Tel Aviv, Israel, developing software for tackling urgent and complex challenges in Quantum Computing development.  Classiq’s software increases the level of abstraction and permits developers to implement programs without the need to design the specific gate-level quantum circuits, enabling programmers to accelerate the development of algorithms without the need and expense of manually coding every step and function. 

In these early days of QC, where machines have dozens or maybe 100’s of qubits, many early users are able to write the code themselves, although there are few people that can manually create, test and maintain an efficient circuit with more than 25 qubits.  And as QC’s scale to 1,000 or 10,000 qubits or more, manually writing code for every qubit and every function will be practically impossible, so the Classiq algorithm design platform will be increasingly vital to users of QCs.  And on top of the general efficiencies that Classiq can provide, their platform searches for solutions over a huge design space, meaning it can explore many more configuration options than a human can (see more details here).  This leads to circuits that are more compact and efficient and results that are more accurate since fewer gates can but used.  Another unique and constructive feature of the Classiq platform revolves around their allowing customers to use the platform to build their own modular functional blocks, which they can reuse together with blocks provided by Classiq.  This enables/allows customers to build and own their own IP without needing to risk IP leakage to external providers.  As companies build proprietary quantum software applications, enabling this IP protection will be essential.

Their Quantum Algorithm Design (QAD) platform is the quantum equivalent of computer-aided design (CAD) enabling quantum software engineers and scientists to produce algorithms much faster than ever.  As quantum circuits become increasingly larger and more complex, it becomes difficult or impossible to design them manually.  The QAD automatically synthesizes complete quantum circuits from high-level functional models in seconds and does so in an elegant and easy to utilize visual framework.  It is platform agnostic so can work with any gate-based QC, most major quantum cloud providers and can output its code in various quantum languages including Qiskit, Q#, Cirq and others.  And while some development tools provide certain templates, such as VQE (variational quantum eigensolver, used for things like preparing the ground state of a given molecule to aid in drug discovery among many other uses), they are very difficult to customize.  Similarly, search algorithms require an oracle function which is nearly impossible to create manually but very easy with QAD.  Look to this site for a future feature post about Classiq, but in the meantime, I encourage you to visit their website and sign-up for updates. They offer frequent webinars and informal demonstrations and other ways to learn more about their products.  They also host “The Qubit Guy’s Podcast,” an excellent weekly discussion led by CMO Yuval Boger, which you can access here or wherever you listen to your podcasts.

Select Collaborations

Now that you have a general understanding of these three profiled companies and their businesses, I want to describe a few of their collaborations to emphasize the interdependence and cooperation that is spurring advances in Quantum Computing.


This past December, ColdQuanta and Strangeworks announced the addition of the forthcoming Hilbert Quantum Computer to the Strangeworks Ecosystem.  Hilbert will be available for early access by select members of the Strangeworks Backstage Pass program with general available later this year.  As noted above, the Backstage Pass program is a vital tool for early development and evaluation of new QC capabilities, and ColdQuanta is benefiting from important feedback in advance of its broader public release.  Think of it as a beta release which is accessible to an optimal set of users and therefore able to provide deep insights on strengths and weaknesses of the system.


In January of this year ColdQuanta and Classiq announced a partnership to make 100-qubit quantum circuits a reality for companies and researchers seeking quantum computing solutions.  The partnership combines ColdQuanta’s cold atom quantum computers and Classiq’s quantum algorithm design software.  They aim to provide customers with the ability to create, simulate and execute unique quantum circuits to address a wide range of finance, material science, supply chain, and machine learning challenges.  As Nir Minerbi, CEO of Classiq noted, “as the industry moves from toy problems solved by toy circuits running on small quantum computers to solving real problems that require complex circuits on larger quantum computers, there is an acute need for a high-level platform to develop these circuits quickly and efficiently.”  By entering into this partnership now, the companies should be well aligned to scale together as ColdQuanta releases larger QCs in the future.


Last month Classiq announced a collaboration with NVIDIA to bring large-scale quantum circuits to customers intended to enable the exploration of the benefits of larger quantum circuits before the actual quantum hardware is available.  To create and debug the next generation of quantum algorithms, customers need to simulate larger and more sophisticated quantum circuits.  NVIDIA has developed the cuQuantum software development kit to speed up quantum circuit simulations based on state vector and tensor network methods.  By combining with Classiq, NVIDIA hopes that cuQuantum users can write more sophisticated solutions which can be created, debugged, stress-tested and scaled in preparation for eventual availability of quantum hardware that can execute them.


Late in 2021 Classiq announced a collaboration with NTT DATA to implement novel credit risk analysis algorithms using quantum computers.  Credit risk analysis is vital in determining the creditworthiness of borrowers or vendors to quantify and limit the risk of loss to the lender.  As Shunichi Amemiya, Head of R&D for NTT DATA noted “we are interested in applying quantum computer technology to financial engineering…Classiq’s platform enables us to effectively generate and run quantum algorithms for the issues in applying to our use case.”  This is important in that it represents a collaboration between a QC company and industry, with very specific use cases in mind.  As more and more companies consider QC applications for their industries, I expect to see more of these types of collaborations and believe Classiq offers a unique value proposition for accelerating QC engagement by industry.

ColdQuanta/IBM Q

In May of last year, ColdQuanta announced that it had joined the IBM Quantum Network and would be integrating IBM’s Qiskit open-source software development kit (SDK).  ColdQuanta plans to make its Hilbert QC available via IBM Q, IBM’s quantum network, and combined with its integration with Qiskit, will enable ColdQuanta customers to accelerate their quantum computing initiatives.  The companies also noted that they will pursue joint development opportunities with the goal of accelerating the adoption of other quantum technologies. 


Based on the open-source nature of many of the aspects of the QC ecosystem and as this post highlights, ‘collaboration’ is the modus operandi in Quantum Computing at present.  While some companies may enter such arrangements largely for the press release, there are many advantages in uniting best-in-class players to create synergies between areas of expertise.  I am certain we will see more and more such collaborations in the future, not to mention more formal M&A activity.  As the market matures and these companies begin generating meaningful revenues, I expect this spirit of openness to begin to fade as companies compete for customers.  But in the meantime, I look forward to following the various collaborations, partnerships, and other alignment arrangements.

Disclosure: The author has no beneficial positions in stocks discussed in this review, nor does he have any business relationship with any company mentioned in this post.  The views expressed herein are solely the views of the author and are not necessarily the views of Corporate Fuel Partners or any of its affiliates.  Views are not intended to provide, and should not be relied upon for, investment advice.


I want to thank Paul Lipman of ColdQuanta, William Hurley of Strangeworks and Yuval Boger of Classiq for their generous time and input on this post.

Lipman, Paul.  Interview.  Conducted by Russ Fein, March 9, 2022.

Hurley, William.  Interview.  Conducted by Russ Fein, March 29, 2022.

ColdQuanta and Strangeworks Announce Addition of Hilbert Quantum Computer to Strangeworks Ecosystem,” PR Newswire, December 21, 2021.

CLASSIQ and ColdQuanta Partner to Provide a Complete Solution to Creating and Executing 100-Qubit Quantum Circuits and Beyond,” Businesswire, January 25, 2022.

CLASSIQ Collaborates with NVIDIA to Accelerate the Path to Quantum Advantage,”Businesswire, March 24, 2022.

CLASSIQ, NTT Data Collaborate on Quantum Algorithm Design for Financial Modeling,” Businesswire, December 1, 2021.

ColdQuanta Joins the IBM Quantum Network, Bringing Cold Atom Quantum Technology to Growing Ecosystem,” Global Newswire, May 26, 2021.

If you enjoyed this post, please visit my website and enter your email to receive future posts and updates: http://quantumtech.blogRuss Fein is a venture investor with deep interests in Quantum Computing (QC).  For more of his thoughts about QC please visit the link to the left.  For more information about his firm, please visit Corporate Fuel.  Russ can be reached at

Quantum Computing Jobs

While I might be dating myself with this reference, a formative film of my generation is “The Graduate,” starring a very young Dustin Hoffman as Benjamin Braddock.  There is a scene at Ben’s college graduation party when Ben is given advice by Mr. Maguire, his dad’s friend:

Mr. Maguire:   I want to say one word to you, Benjamin.  Just one word.

Ben:   Yes, sir.

Maguire:   Are you listening?

Ben:   Yes, I am.

Maguire:   Plastics.

Ben:   Exactly how do you mean?

Maguire:   There is a great future in plastics.  Think about it.  Will you think about it?

Ben:   Yes, I will.

Maguire:   Okay.  Enough said.  That’s a deal.

If this film were made today, I expect that “Plastics” would be substituted with “Quantum Computing.”  It seems today as if the business world is exploding with Quantum Computing (QC) activity, including $3.5 billion in raised and announced start-up funding in 2021 and $30 billion in total public funding announced through 2021.  This has led to a large and growing ecosystem of global players including 196 start-ups, 17 incumbents, 57 public/government agencies and 169 academic groups. [McKinsey & Company, 2022].  These various and disparate entities are all in a mad scramble to attract talent, and while “quantum physics scientist” and “quantum programmer” are obvious categories of need, the diversity of skills and talent currently being sought for positions in QC organizations is quite broad.  Here is a summary of advice I have for players in, or interested in entering, the QC world:

Let’s unpack some of this with some additional details (including many embedded hyperlinks which I hope you find useful).


Companies actively in the QC space, and those considering bringing QC capabilities to their businesses should do so with urgency.  This is a fast-evolving space and laggards risk missing the boat or not being prepared once QC resources are able to achieve things unachievable today.  In fact, this is becoming a growing focus and is beginning to make its way to the boardroom.  Karina Robinson, CEO of Robinson Hambro Ltd., a firm providing CEO/Advisory services and Board search, organizes the City Quantum Summit, an annual Quantum event held at the Lord Mayor of London’s Mansion House, intended to attract CEO’s and CTO’s.  She believes that “this is the year where it really will become a board issue and recommends that most large companies will need a non-executive board member that understands Quantum Computing and is able to talk with the CTO about it.”  The next summit will take place on October 11, 2022, and you can learn more about it here.

With an ever-expanding number of start-ups, companies, universities, large corporations and government agencies focusing on quantum technologies, the problem of a skills shortage cannot be ignored. “QURECA (Quantum Careers and Resources) is the first online training and recruitment platform that provides online courses and resources to fill the gaps in the existing quantum community.  With a mission to create global opportunities in quantum technologies and train the future quantum workforce QURECA has launched a non-technical course ‘Quantum for Everyone’, a specialized series in Quantum Finance in collaboration with QuantFi, and its educational platform has been used by global quantum companies such as Zapata Computing.”  [Kaur and Venegas-Gomez, 2022]. 

Another QC advocate, Terrill Frantz, Professor of eBusiness and Cybersecurity and Quantum Information Science at Harrisburg University of Science and Technology, hosts a number of QC events and databases including ‘Quantumapalooza’, which is a dynamic listing of free online learning resources in Quantum Computing, currently listing 18 different courses and events.  I encourage companies to explore resources such as QURECA and Quantumapalooza and the many others available on-line [additional examples noted in the graphic below] to expand their internal knowledge about QC and to offer such resources to their employees. 

Source: Kaur and Venegas-Gomez, 2022

Given the broad and evolving QC landscape and the need to find the best talent in terms of fit, I also suggest utilizing a professional recruiter/headhunter to fill key roles.  Be prepared for the search to take quite some time (i.e, 3-6 months), but leveraging a professional headhunter should make the process as efficient as possible.  Some firms that specialize in quantum-related hiring include Quantum Futures, Entangled Positions, and Robinson Hambro among others.

Job Seekers

According to Connor Teague, President and Quantum Talent Partner of  Quantum Futures, a London based staffing and recruiting firm, “Last year there was an influx of quantum algorithm hires and demand for PhD’s in physics.  There recently has been a transition to hardware, anyone coming from a master’s in electrical engineering or even a bachelor’s.”   That trend away from advanced and specialized degrees is a natural effect of the talent shortage in Quantum, and this shortage is now extending throughout quantum businesses.  According to a paper published in August 2021, “we find a range of job opportunities from highly specific jobs, such as quantum algorithm developmer and error correction scientist, to broader job categories within the business software and hardware sectors.  These broader jobs require a range of skills, most of which are not quantum related.”  [Hughes et al, 2021]  

As Herman Collins, executive search specialist and CEO of Herman Collins LLC and StrategicQC LLC (see more about Herman and his firm here) noted, “what you’re seeing is diversification of positions in terms of marketing, business operations, finance, etc.  And that just reflects a broadening of the acceptance of Quantum in the business community.  And that continues to grow.” Now that QC has begun to evolve out of the lab and into industry, businesses are finding themselves needing to fill out their staffs with lots of non-quantum roles.  “Business developers on the path to quantum revenues, marketing types to share the quantum message in a common language manner, conventional computer scientists who can create seamless integration with quantum, human resource staff to fill the growing number of roles – all crucial to the achievement of quantum success.” [Lenahan, 2021]

For those of you interested in transitioning your career into QC, Connor Teague suggests you “get messy on GitHub…show your future employer that you’ve been consistently trying to upskill after hours.”  Getting certifications and/or simply taking on-line courses about quantum computing will help differentiate candidates. 

Herman Collins added “An ideal situation would be to go to your current employer and say, ‘I have an interest in this area and this is how it might affect our company going forward…and someone here should be involved and knowledgeable about it and I’d like to be that person.’  Get your current company to invest in you, and, de facto invest in the company by having you as a resource.”  Perhaps such a company will allow you to take classes on company time and/or help pay for courses that aren’t offered free.

The Quantum Computing Report, a leading voice in Quantum Computing since 2015 and published by managing editor Doug Finke, is another terrific compendium of all things quantum including a robust set of job postings. You can see their US/Canada Jobs Page here, and a separate Europe/Asia/Australia list here

Another resource led by Terrill Frantz and Harrisburg University of Science and Technology, is the QED-C “Quantum Jobs” database, which lists job posting by QED-C member including corporations, academic institutions, national laboratories and government agencies working in quantum.  As of 4/2 there were 640 open positions posted on this site, a good number of which are for non-technical hires.  In addition, a quick scan of the “Careers” section of many of the large QC players, highlights a larger number of total openings and a growing number of non-science roles, highlighted in the table below.

Non-science Quantum Jobs

This is just a representative sampling.  Many additional companies and positions are listed among various job-related resources.


Naturally, the best way to optimize your chances of getting a prime role at a QC company would be to get a QC related PhD.  When I asked Herman Collins for advice to college students he said, “I would tell them to position themselves where they had the greatest leverage, combined with their own personal interest.  The greatest leverage comes from having a PhD in physics.  It gives you the greatest leverage and the greatest bargaining chips and is the most sought after.  Do you need one?  Certainly not, but it does pay. It does pay to have one.”

The next best thing other than a PhD would be a Masters degree.  However, Terrill Frantz, who also maintains the only bona fide list of all of the master’s degree level QC programs in the world notes that as of today there are only 37 programs listed.  And at the Bachelor’s level, many colleges do not offer a QC degree to undergraduates, but are increasingly adding curriculum.  However, most offer courses in quantum mechanics in the physics department and various computer classes within the computing department, so considering a dual major and/or seeing if you school would offer or create a degree leveraging those two departments should be considered.

As Araceli Venegas-Gomez of QURECA noted, “We of course provide recruitment services, but we also provide education – those additional skills.  We prepare people and we place them into specific projects which we call Fellowships.  We enable companies unsure about hiring someone to enable them to work on a project and see if there is really value added to the company.  That person then also has an opportunity to see if they enjoy working in industry or whether instead, they should stay in academia.”  So finding internships or summer programs or “Fellowships” is a great way to formally explore a careering in QC before actually taking the full-time plunge.

If you do opt to join the QC workforce out of school, as Karina Robinson noted, “In a growing industry, if you get in early you can shape your job.  And when you’re working at a small firm that is growing you end up doing a bit of everything, whatever your job title is.  And therefore it can help you develop a career path which may end up at some other deep tech company or you may go into a think tank or all sorts of other things.  But whatever you do with Quantum will be useful, whether you continue in Quantum or go into another sector.”


So when I get invited to college graduation parties, I will be whispering “Quantum Computing” into the ears of the recent graduates.  It’s also great advice to those seeking a change in their current career and while technical aptitude and experience create the best fit for most of the current QC industry positions, there is a growing number of non-technical jobs in QC as well.  There are many great resources available to you as you consider a QC career and I hope you take advantage of them and consider joining this exciting and evolving field.

I want to thank Christopher Bishop, the host of Inside Quantum Technology’s QuantumTechPod (among Chris’s various activities), for his generous time, input and suggested introductions for this post, and for providing a template for the non-quantum job postings included herein.


Teague, Connor. President and Quantum  Computing Talent Partner, Quantum Futures.  Interview. Conducted by Russ Fein, March 9, 2022.

Robinson, Karina. CEO of Robinson Hambro Ltd. Interview.  Conducted by Russ Fein, March 14, 2022.

Frantz, Terrill.  Professor of eBusiness and Cybersecurity; Quantum Information Science, Harrisburg University of Science and Technology.  Interview.  Conducted March 14, 2022.

Venegas-Gomez, Araceli.  Founder and Director of QUERECA.  Interview.  Conducted by Russ Fein, March 15, 2022.

Collins, Herman.  CEO of Herman Collins LLC and StrategicQC LLC.  Interview.  Conducted by Russ Fein, March 16, 2022.

Leddy, Chuck, “Q&A: The talent shortage in quantum computing” MIT News, January 23, 2019

Lenahan, Brian, “Quantum Talent – Shortages and Tactics” Quantum Strategy Institute, December 3, 2021

C. Hughes, D. Finke, D. German, C. Merzbacher, P. Vora and H. Lewandowski, “Assessing the Needs of the Quantum Industry” Fermilabs/physics.ed-ph, published on arXiv August 25, 2021

Flaherty, Nick. “US summit on quantum industry highlights skills shortage” eeNewsEurope. October 11, 2021 

Closing the quantum computing talent gap”, August 4, 2021

Quantum Computing Grows Its Following with Investments and Experiments on the Rise, According to IDC” May 19, 2021

Hughes, Owen. “Quantum computing skills are hard to find. Here’s how companies are tackling the shortage” ZDNet. November 12, 2021 

McKinsey & Company, “The Rise of Quantum Computing,” from their webpage accessed April 2, 2022.

The Quantum Computing Report, “Jobs” website page, accessed April 6, 2022

If you enjoyed this post, please visit my website and enter your email to receive future posts and updates: http://quantumtech.blogRuss Fein is a venture investor with deep interests in Quantum Computing (QC).  For more of his thoughts about QC please visit the link to the left.  For more information about his firm, please visit Corporate Fuel.  Russ can be reached at

What is Sandbox AQ?

On March 22, 2022, Sandbox AQ announced its formal launch as a separate company from Alphabet (fka Google).  I consider this to be seminal event in the Quantum Computing (QC) industry so wanted to provide additional color to emphasize the magnitude of the announcement.

As the press release from the Company notes, Sandbox AQ aims to combine Artificial Intelligence (AI) with Quantum Computing technologies in a software-as-a-service (SaaS) model aimed at Global 1000 companies.  There is growing focus at the intersection of these two cutting-edge technologies (AI and QC) whereby it is expected that QC can enhance and accelerate the powers and capabilities of AI, so this dual-focus makes sense (see my prior post “At the Intersection of QC, AI and Machine Learning” for more on this topic).

Overtly, Sandbox AQ is an enterprise SaaS platform, led by CEO Jack Hidary, who is an extremely accomplished thought leader in Quantum Computing and accomplished entrepreneur.  The fact that it was created at Alphabet, is being led by Jack, and is very well funded, would alone be sufficient to grab the attention of the QC industry.  However, there is a lot more to unpack from the announcement, and which I hope to convey in this post.  Namely, the company was incubated and spun-out from a preeminent force in the emerging QC world, is being led by Jack Hidary and Chaired by Eric Schmidt (former CEO/Chairman of Google, former CEO/Chairman of Novell, former CTO of Sun Micro), has received multiple-$100-million in initial funding from a list of preeminent venture investors, has an illustrious Advisory Board and has world-class customers on day one.

And, interestingly, Sandbox AQ is a software enterprise and NOT a hardware provider – the Google team, which famously (although not without controversy) achieved quantum supremacy in 2019 with their 53-qubit Sycamore machine, remains within the Google X division (the division affectionately known as “the moonshot factory”) and continues to push the envelope in QCs of the superconducting variety.  By excluding the Santa Barbera/Venice Beach QC hardware team, Sandbox AQ can be hardware agnostic.

Sandbox AQ begins as an independent company focused on two major initiatives: post-quantum cryptography (PQC) and quantum sensors.  Any day now it is expected that NIST (Nat’l Institute of Standards and Technology) will announce their selection of the Round 3 candidate algorithms from among 7 finalists and 8 alternates, with a finalized standard expected by 2024.  Such algorithm(s) are intended to replace RSA which dominates today’s public key cryptography, but which will be vulnerable to powerful QCs. Sandbox AQ has developed an AI based discovery engine that will find all the places where an enterprise needs to upgrade the cryptographic software to a quantum secure PQC solution [Quantum Computing Report, March 25, 2022]. On the sensor front, quantum-based sensors can provide novel navigation systems based on geophysical signals (such as local variations of magnetic and gravitational fields) and are not dependent on satellites.  This will be vital for autonomous vehicles and is of interest to the military for navigation in instances where GPS is unavailable or denied.  There are also quantum sensor applications in medical diagnostics and at the discovery of novel materials, including pharameuticals, using AI [Spencer, 2022]. 

Let’s drill down further so you can appreciate the stature of this announcement.


As noted above, Sandbox AQ is being led by Jack Hidary as CEO and Eric Schmidt as Chairman.  Here is some more color on each:

Jack Hidary:  Jack is a serial entrepreneur, technology researcher and accomplished author.  He founded EarthWeb/Dice, which had a series of venture rounds before going public.  He also co-founded Vista Research which he sold to S&P/McGraw Hill.  He is a board member of the XPrize, a Sandbox Group Director at Alphabet and founder of the Jack D Hidary foundation, among other activities.  His book “Quantum Computing: An Applied Approach”, published by Springer and now in its second edition, is a leading textbook for many universities and training programs and he has collaborated with MIT on a series of academic papers focused on deep learning.  Jack studied neuroscience at Columbia and received the Stanley Fellowship in Clinical Neuroscience at NIH where he worked on functional brain imaging and artificial neural networks.

Eric Schmidt: was the Emeritus Chairman at Alphabet. He was responsible for the external matters of all of the holding company’s businesses, including Google advising their CEOs and leadership on business and policy issues. Prior to the establishment of Alphabet, he was the chairman of Google for four years. From 2001-2011, he served as Google’s chief executive officer, overseeing the company’s technical and business strategy alongside founders Sergey Brin and Larry Page. Under his leadership, Google dramatically scaled its infrastructure and diversified its product offerings while maintaining a strong culture of innovation, growing from a Silicon Valley startup to a global leader in technology. Prior to joining Google, he was the chairman and CEO of Novell and chief technology officer at Sun Microsystems. Previously, he served on the research staff at Xerox Palo Alto Research Center (PARC), Bell Laboratories and Zilog.  Mr. Schmidt is a Co-Founder of TomorrowVentures, a Co-Founding Partner at Innovation Endeavors and serves as Board Member at Abacus.AI. He also is a Co-Founder and serves as director of The Schmidt Family Foundation and serves as Chairman at Broad Institute. He holds a bachelor’s degree in electrical engineering from Princeton University as well as a master’s degree and Ph.D. in computer science from the University of California, Berkeley. He is a member of the President’s Council of Advisors on Science. He was elected to the National Academy of Engineering in 2006 and inducted into the American Academy of Arts and Sciences as a fellow in 2007

Initial Investors

Sandbox AQ completed a recent funding round and received “hundreds of millions” (i.e., nine-figures) from several investors (and I presume Alphabet also made a significant initial investment).  It is a strong and illustrious group which not only brings capital to the business, but also a strong network of connections.  Here are some details:

Bradley BloomCo-founder and senior advisor at Berkshire Partners, Advisory Board Member at New Era Capital.  Former partner at Thomas H. Lee Co. 
Breyer CapitalLed by James Breyer the co-founder and CEO.  Mr. Breyer is a Board Member at The Blackstone Group and has been an investor in over 40 consumer internet/technology companies.
David SiegelCEO and Director of Meetup. Former CEO of Investopedia, former President of Seeking Alpha, former SVP 1-800-Flowers and former GM at Duane Reade.
Eric SchmidtFormer Chairman and CEO of Google, additional details noted in prior section.
First Light CapitalA women-led investment firm focused on seed investments in technology and pharma.
Guggenheim Inv.Led by Scott Minerd, Managing Partner and Global CIO.  Former MD with Credit Suisse First Boston.
Parkway VentureA NYC/Boston venture firm with interests in software, SaaS, AI and Machine Learning.
Section 32A Los Altos, California venture firm that invests in healthcare, information technology, life sciences, SaaS, cybersecurity, AI, ML, and other technology-based sectors.
Thomas TullFormer founder, Chairman and CEO of Legendary Entertainment where he pioneered the use of data analytics to improve the way movies are marketed.  Also founder of Tulco LLC, which seeks to invest in AI, ML and predictive data analytics.
TIME VenturesLed by Marc Benioff, Co-Founder, Co-CEO and Chairman at  Member of the World Economic Forum Board of Trustees.
T. Rowe PriceAn asset management firm based in Baltimore, MD, with over $1.6 trillion in assets under management.

Advisory Board

In addition to strong executive leadership and a well-connected set of investors (some of which also serve on the Advisory Board), Sandbox AQ has announced a diverse and powerful Advisory Board which includes the following:

Marc PoratSerial entrepreneur of six ventures including General Magic (voice infrastructure software) and PlanetX
Wendy Tan WhiteCEO, Intrinsic (robotics software and AI), former VP at X, the moonshot factory
Dr. Taher ElgamalCTO, Security at; Co-founder of Nok Nok Labs and InfoSec Global; renowned cryptography hailed as the “father of SSL”
Dr. Yossi MatiasVP, Engineering & Research at Google
Dr. Andrew ConradCEO, Verily (Google X’s Life Sciences org) and Co-founder of the National Genetics Institute
Dr. Fei-Fei LiSequoia Professor of Computer Science at Stanford University; former VP/Chief Scientist of AI/ML at Google Cloud
Dr. Ashton CarterFormer US Secretary of Defense; Director of the Belfer Center for Science & Int’l Affairs at Harvard University’s Kennedy School
Susan M. GordonFormer Principal Deputy Director of National Intelligence
Admiral Mike RogersRetired Admiral, former Commander of US Cyber Command, and Director, National Security Agency.  Senior Fellow and Adjunct Professor in the Kellogg Executive Leadership Institute at Northwestern University
Admiral John RichardsonRetired Admiral and former Chief of Naval Operations, US Navy
Blythe MastersFounding Partner at Motive Partners
Frank VetteseCo-founder, SummitNorth capital and advisory firm; former CEO of Deloitte
David SiegelComputer scientist, entrepreneur, co-founder and co-CEO, Two Sigma, a quantitative trading firm managing $60 billion; MIT Board Member
John Seely BrownFormer Independent Co-Chair of Deloitte’s Center for the Edge; former Amazon board member and Chief Scientist, Xerox PARC

While it is unclear how actively involved this Advisory Board will be, given that it was announced as part of the spin-off, it’s likely it is an integral part of the business.


In addition to the marquee leadership, investors and advisors, Sandbox AQ also begins with substantial clients on day one.  Specifically, the following were included with the Company’s opening press release:

  • Vodafone Business (global technology communications company)
  • Softbank Mobile
  • Wix (Cloud-based development platform)
  • Mount Sinai Health Systems
  • US Federal Government

That last name on this is a bit cryptic but given the noted programs in quantum sensing and their applicability to defense, this is no surprise.  Beyond these initial customers, the company is aiming to provide quantum and AI solutions to the Global 1000 enterprises and his strategic alliances with some powerful professional services firms which it plans to announce soon.  Sandbox AQ is also in close collaboration with faculty partners at leading universities and plans to invest in Ph.D. students and to host postdocs.


Sandbox AQ begins life as an independent company with an enviable roster of executives, investors, advisors and customers, not to mention a sizable war chest.  By combining QC and AI, they are at the forefront of both emerging technologies, and by remaining hardware agnostic, they preserve the ability to focus wherever the latest hardware breakthroughs come from.  As noted by Eric Schmidt: “the convergence of quantum and AI technologies is already transforming entire industries, accelerating scientific discovery and reimagining what we thought was possible.”  I look forward to seeing how Sandbox AQ leverages its substantial resources and connections to help companies navigate and excel in this rapidly changing world, and predict Sandbox AQ will remain a force to be reckoned with.

Disclosure: I have no beneficial positions in stocks discussed in this review, nor do I have any business relationship with any company mentioned in this post.  I wrote this article myself and express it as my own opinion.

References:, accessed March 27, 2022

Sandbox AQs Product Strategies,” Quantum Computing Report, March 26, 2022

Adebayo, K. S., “Sandbox AQ’s convergence of quantum and AI will transform the enterprise,” VentureBeat, March 23, 2022.

Spencer, M., “What is Sandbox AQ?”, IPO Times/Quantum Foundry, March 22, 2022

Hidary, J., “Sandbox AQ – The Launch,” Sandbox AQ press release, March 22, 2022

If you enjoyed this post, please visit my website and enter your email to receive future posts and updates: http://quantumtech.blogRuss Fein is a venture investor with deep interests in Quantum Computing (QC).  For more of his thoughts about QC please visit the link to the left.  For more information about his firm, please visit Corporate Fuel.  Russ can be reached at

The Quantum Computing Elevator Pitch

Readers of this blog may have read prior posts where I attempt to summarize the features and benefits of Quantum Computing (QC) that underly its ability to transform computing with enormous power and potential.  I have tried to do so without layering in too much math or physics and I hope you have found these posts helpful.

In this post I will summarize the key aspects of QC in an “elevator pitch” without any math or physics, intended to help pique your interest in digging in and learning more. Before I launched this site/blog, I wanted to understand some of the underlying quantum physics, linear algebra, computational theory and related physics associated with qubits to satisfy my own curiosity, but none of that is essential to appreciate the power of QC.  Here is the “pitch” (useful if the elevator is only going up a couple of floors):

Quantum Computers will transform the way we use computers by massively accelerating certain computations and, more importantly, by enabling a wholly new form of computing.

That is the headline and take-away message.  Many have conveyed the first part (speedup of computing), but the second part is less noted, although more potent.  Here are some further details to support this statement (if the elevator trip is slightly longer):

  • Taming tiny particles (atoms, electrons, photons, etc.) enables transformative computational power. 
  • At this tiny scale, matter behaves both like a particle and a wave.  We are familiar with both but have challenges understanding and describing actions where particle and wave features happen at the same time, as in QCs.
  • This particle-wave duality underpins the features of superposition and entanglement, which is where the power of QC is derived.  Superposition simply means that each computing bit or ‘qubit’, can be either 1 or 0 or a combination of both.  Entanglement simply means that qubits can be connected and dependent on each other, enabling simultaneous processing/computation.
  • Mankind has refined this ability to control these tiny particles over the past 100 or so years, and actual working quantum computers now exist and can be accessed today over the cloud, albeit these early QCs do not yet contain more power than classical computers.
  • Now that QC has moved from being theoretical, to being practical, billions of dollars and enormous resources are being funneled into the QC space by governments, major corporations, new companies, venture investors and academic institutions, in order to perfect and leverage this new computing power.
  • Over the next few years, QC power should increase to the point where it can be used to understand chemical reactions in a way that leads to new medicines, design highly specialized materials to improve batteries, solar power, fertilizers and other important things, as well as other amazing advances.
  • However, the really exciting applications of QC will not be because QCs can do current computing faster (which is immensely valuable) but that QCs will let us tackle problems that at present we don’t even bother trying with regular computers because we know they are much too hard, or enable us to provide answers to questions we haven’t even thought to ask. [Rudolph, 2017]

This is the spine tingling, wide open blank canvas, that makes Quantum Computing so exciting for me (and by the time you finish this post, hopefully for you too).

Now, let’s break this down into a few baby steps.

You don’t need to understand the physics of most technologies in order to use and benefit from them

New technologies are hard to appreciate and understand but can be transformative to society even without a user’s understanding of how they work.  Many people that are new to QC get tripped up trying to understand exactly how the underlying quantum mechanics “work”.  Unfortunately, understanding the quantum physics is extremely challenging because it involves a scale so small that it is not easy to relate to using our current person-sized orientation.  However, if you think back to prior transformational technologies, you likely will note that most were (and still are) not understood by lay people.  Here are a few examples:

  • Radio (and television)
  • Electricity
  • Integrated Circuits

In the late 1800’s man mastered “waves” in order to transmit voice signals across long distances.  We are all familiar with AM/FM radios but likely do not understand the physics of “amplitude modulation” for AM or “frequency modulation” for FM.    We cannot see radio waves with our eyes.  Yet we can all enjoy listening to music in our cars or speaking with loved ones on our phones.

Also in the late 1800’s (specifically on September 4, 1882) New York City was illuminated by electrical light for the first time, showcased by the lighting of the New York Times building (as depicted in episode 7 of the Gilded Age on Hulu).  During the subsequent decades, there was raging debate about the dangers of electricity and the relative benefits/weaknesses of AC vs DC power.  Despite these debates, electrical wires were installed throughout the country/world and today’s electrical grid is a complex and inter-connected wonder.

You take for granted that you can plug a lamp into a power socket in your home, and instantly have “light”.  You may not realize that the electrons flowing over the filament in the bulb arrived there nearly instantly from many, many (maybe thousands of) miles away.  You do not need to know anything about electricity to turn on that lamp and enjoy its benefits.  Similarly, when PCs were first released, many critics were baffled why anyone would want to buy a home computer, and certainly did not understand how they worked.  Yet today, nearly everyone has access to PCs and they are ubiquitous in business and education.  People use them daily without any underlying appreciation for how integrated circuits function.

The power and potential of QC has to ability to rival the transformative impacts of radio, electricity, and integrated circuits, and it will do so whether users understand the inner physics or not.

What’s with the Particle/Wave Duality?  I thought you promised no math or physics…

Yes, I understand this sounds very “science-y” but it is quite straight forward.  We are all familiar with the way particles behave.  We don’t have to understand the underlying Newtonian physics to appreciate the way billiard balls move on the pool table, the way the golf ball travels when you hit it, or for that matter, the way the tides happen in response to the movement of the moon.  The actions and reactions of “particles” that we experience in our frame of reference is intuitively understood even if the underlying physics are not.

Similarly, we understand and appreciate the behavior of waves.  If we throw two stones into a pond we can see the resulting ripples (waves) in the water and we can see how those waves interact with each other.  We can wave a streamer and see the waveform in the ribbon.  And for anyone who has used noise-cancelling headphones, we can appreciate how the noise is removed once we engage that feature.  The way this noise cancellation happens is the unit is “listening” and then creates a sound wave that is opposite to the sound it hears.  When the two “waves” are both transmitted to the ear, one out of phase (opposite) with the other, they “cancel” each other and we hear silence.

In Quantum Computing, the physical qubits (electrons, atoms, or photons, etc.), because they are so tiny, behave a bit like particles and a bit like waves.  It is not important to understand precisely how, but the “wave” aspect, similar to the “cancellation” that happens in noise cancelling headphones, is part of what empowers Quantum Computers to process information so much faster than classical computers by amplifying what is being sought and/or cancelling out what is not. 

Transformative technologies create transformative wealth

It is certainly not an overstatement to say that integrated circuits have wildly transformed society.  There are now integrated circuits in nearly every powered device.  Everything from kids’ toys to smart thermostats to cell phones is dependent on integrated circuits. And this transformation is evidenced in enormous wealth creation.  Look at the top ten companies, by market capitalization (as of 3/20/22):

  • Apple ($2.8 trillion)
  • Saudi Aramco ($2.3 trillion)
  • Microsoft ($2.2 trillion)
  • Alphabet/Google ($1.8 trillion)
  • Amazon ($1.7 trillion)
  • Tesla ($1.0 trillion)
  • Berkshire Hathaway ($771 billion)
  • NVIDIA ($675 billion)
  • Meta/Facebook ($608 billion)
  • Taiwan Semiconductor ($555 billion)

Seven of these top ten companies are directly in the “integrated circuit” business, either as a manufacturer or for their primary value proposition (Aramco, Tesla and Berkshire Hathaway are the exceptions, although you might argue that Tesla’s could not operate without integrated circuits and Berkshire has large positions in tech companies).  These seven companies have created over $10 trillion dollars in wealth, which is a staggering amount, and this has been done in an astonishingly short period of time.  Those seven companies combined are larger than every country on the planet (as measured by GDP) with the exceptions of the US and China.  I won’t be so bold as to definitively say that QC will do the same (or more accurately, won’t predict when), but the potential for QC to create these levels of wealth is certainly possible.

A Quantum Computer, by definition, is a Computer.  What is so Different?

Today’s personal computers are awesome and have power that may have been considered unimaginable just a few decades ago.  My current PC (a Dell OptiPlex 7780) has 64 GB of RAM and can operate at 2.90GHz.  That means its RAM or rapid access memory (the playing field of the computational power of my machine) has 64,000,000,000 bytes of computing memory or 64 billion units. It can process those bytes at the rate of 2.9 billion per second.  Think about that.  My basic desktop computer has billions of computing units and can process them at the rate of 2.9 billion per second.  That means that in the 6 seconds it took me to type this sentence, my computer could perform over 17 billion calculations.

That sounds unfathomably powerful and fast to me, and it is.  So why could we possible need even faster calculations and what can’t I already do on my Internet-connected machine that I might want to do?  Answers to that question are where Quantum Computing gets fun and exciting.  The purpose of today’s post is not to explain all of the details, physics or specific use cases, but rather to excite you enough to want to learn more about those things.  So, let’s change the perspective a bit (pun intended for those of you familiar with the Hadamard gate).

Because QCs approach calculations differently and can utilize entangled qubits, it approaches calculations/algorithms differently from classical computers.  As a reminder: a) qubits operate in three dimensions; b) QC gates are more complex than the AND/NOT/OR gate functions of classical computers; c) quantum algorithms are bi-directional; and d) results are probabilistic (not deterministic) [see here for prior post which explains these features in greater detail]. 

So what do these features do that make QCs so much different than classical computers?  Let’s use an analogy to help convey this.  Classical computers are a bit like radios that existed before television.  You could listen to sports events live without going to the stadium. You could hear the news and listen to stories about faraway lands.  And you could be entertained for hours.  These were deeply satisfying activities in the day but compare those machines with today’s high definition big screen televisions and the evolution of the information and entertainment is massive.

To extend this thought exercise a bit further, consider the evolution of the Internet.  When AOL was distributing their enrollment CDs seemingly everywhere and was the first company to make e-mail a household thing, most people did not imagine such technology would also let you sell your junk (eBay), get a ride to the airport (Uber), or find the answer to nearly any basic question nearly instantly (Google).

Putting it all Together

To paraphrase Terry Rudolph (Imperial College quantum physics professor and co-founder of Psi Quantum), explaining how quantum computers work is a bit like having someone describe van Gogh’s “Starry Night” after only seeing a black and white photograph of it, which has been chewed by a dog.  It is difficult to do it justice. 

There are countless articles about the power of Quantum Computers and the marvels society will enjoy once they are powerful enough.  This is generally framed in terms of combinatorics and what can be achieved if such calculations can be sped up considerably.  I am extremely excited about this aspect of QC and much of my blog writing has been to sing those praises.   However, what is most exciting to me, is to contemplate the “eBays” and “Googles” of Quantum Computing.  What will we be able to do with this completely new form of computing?  What new questions can we ask and have answered?  What types of products, companies and industries will be created?  What programming masterpieces will the programmers create with this new medium? 

I look forward to finding out and hope you continue to join me on the journey.

Disclosure: I have no beneficial positions in stocks discussed in this review, nor do I have any business relationship with any company mentioned in this post.  I wrote this article myself and express it as my own opinion.


Rudolph, Terry, “Q is for Quantum”, 2017

If you enjoyed this post, please visit my website and enter your email to receive future posts and updates: http://quantumtech.blogRuss Fein is a venture investor with deep interests in Quantum Computing (QC).  For more of his thoughts about QC please visit the link to the left.  For more information about his firm, please visit Corporate Fuel.  Russ can be reached at

Learning More About Quantum Computing

Rigetti Computing Chandelier

As I’ve interacted with readers of the Quantum Leap, I’m often awed by the diversity and openness of so many of the folks involved in the Quantum Computing (QC) field.  However, I find that many people new to the field are still put off or intimidated by the concepts covered.  While I have tried to orient my posts so that non-technical readers can still benefit from them, it seems that many well intended, intelligent readers are still baffled by most things “Quantum”.    And while I don’t have a monopoly on the best learning resources, I’ve been on a pretty long and deep dive in my own quantum journey, so wanted to provide links to some resources to help my readers accelerate their quantum educations.  Most of these are available for free although some require modest subscriptions for premium content.

In addition, I’ll provide some guidance as follows:

  • Easy items include topics where ZERO math, physics or prior technical knowledge is needed.
  • Moderate items provide a bit more technical detail but should be approachable for those intellectually curious enough to dig in a bit deeper.  Many refer to some advanced concepts such as Entanglement and Superposition, but no formal math or physics training is required to understand the fundamental details.
  • Advanced items will be reserved for resources that are more technical in nature and/or would take a longer time to get through.

Finally, there is a plethora of resources available on the Internet including articles, podcasts, blogs, interactive learning resources, classes, lectures, seminars, academic papers, etc., so I will also point out a variety of resources and media.  Fortunately, there are many resources, delivered in a variety of ways, so I encourage you to try a few and if they don’t resonate or feel helpful, skip to the next one.  I hope these items accelerate your understanding and spur further learning. This is only meant to be a sampling of resources that I have read or used myself and is not intended to be a complete directory. If any of my readers find additional resources that should be added to this list, please reach out to me at

Essentials of Quantum Computing

For an EASY introduction, consider the following:

For a more MODERATE introduction, the following are included largely because they are longer and therefore take more time to review.  They contain some intermediate concepts but are generally still introductory and should be readable by nearly anyone curious about QC:

  • Research Piece: The Next Decade in Quantum Computing – and How to Play, by Philipp Gerbert and Frank Ruess, Boston Consulting Group, November 2018: a good overview including some technical details and potential use cases.
  • Research Piece: The Coming Quantum Leap in Computing, by Anant Thaker and Suhare Adam, Boston Consulting Group, May 16, 2018: Mostly non-math oriented although there are some graphics depicting speedup, with some intermediate math concepts.  That said, you should be able to absorb the full content without any math background.
  • Research Piece: What Happens When ‘If’ Turns to ‘When’ in Quantum Computing, by Jean-Francois Bobier, Matt Langione, Edward Tao, and Antoine Gourevitch, Boston Consulting Group, July 2021: Another good overview with use cases and some details on various hardware approaches where math is not required.
  • Research Piece: Quantum computing: An emerging ecosystem and industry use cases, McKinsey & Company, December 2021: Another broad overview with details on players and use cases, heavy on details but light on math.
  • Research Piece: Economic-technological revolution through Quantum 2.0: New super technologies are within reach, by Dr. Hermann Rapp, Deutsche Bank, December 17, 2021: A detailed compendium broadly covering QC with very modest levels of technical details or math.
  • Research Piece: Broad Interest in Quantum Computing as a Driver of Commercial Success, by Bob Sorensen, Hyperion Research (sponsored by D-Wave), November 2021: A focus on use cases and potential commercial users.
  • Article: Inside the race to build the best quantum computer on earth, by Gideon Lichfield, MIT Technology Review, February 26, 2020: A broad overview with a deep dive into IBM’s QC history.
  • Article: What is Quantum Computing, CB Insights, January 7, 2021: A good mix of some modest technical details and examples along with a broad overview of use cases.
  • Online Tutorial: Q-CTRL’s Black Opal, a hands-on tutorial with excellent visualizations and short sessions.  There is also a fantastic “Practice” section which is a very visual tool for understanding how gates act on qubits (including animations).  There are three “beginner” modules on Superposition, Qubits and Measurement which I have included here as “intermediate” level.  More advanced modules are shown below.
  • YouTube: “Quantum Computing, Software and Tech” by Anastasia Marchenkova: A charming and approachable series of short videos on various introductory topics and concepts.
  • Book: Quantum Computing for Everyone, by Chris Bernhardt, published 2019: Another good intro book although the “for Everyone” in the title is a bit misleading since it digs in a bit on quantum theory and math.
  • Book: Quantum Boost, by Brian Lenahan, published 2021: A good intro book covering the basics of QC along with some specific applications.

And here are more ADVANCED introductions.  While you don’t need formal linear algebra or physics backgrounds, these get a more technical and introduce some rudimentary linear algebra in the context of describing qubit gates.

  • Article: The Need, Promise and Reality of Quantum Computing, by Jason Roell, published in Medium February 1, 2018: Some good introductions to exponential speed-up, details on qubits, superposition and entanglement and quantum volume.
  • Article: Quantum Computing, by Sarvesh Patil, published in Medium May 16, 2021: Dense but strong overview.
  • Book: Quantum Computing: An Applied Approach, by Jack D. Hidary, published 2019: a great foundational overview with a practical approach to programming and detailed appendices focusing on the QC-specific applications of linear algebra.  There is a more recent new addition as well as a great companion GitHub site (
  • Book: Quantum Computation and Quantum Information, by Michael Nielsen and Isaac Chuang, 10th Edition published 2010: This is one of the most cited books in physics of all time and is a standard college course textbook often referred to as “Mike & Ike”.  I have not read this yet but hope to this year, and include it based on its nearly-universal mention by advanced QC users.
  • Online Tutorial: Q-CTRL’s Black Opal, the hands-on tutorial with excellent visualizations and short sessions noted above.  There are three “Intermediate” modules on Circuits, Entanglement and Noise which I have included here as “advanced” level. 
  • Online Tutorial: Quantum County, by Andy Matuschak and Michael Nielson (of “Mike & Ike” fame), has a novel and engaging method of introducing complex quantum principles including their own “mnemonic” approach which they hope will help users retain the information longer.  While this is included in the “advanced” section of this post, I highly recommend this for readers with some basic working understanding of quantum mechanics.

Hardware/Software/Key Players/Use Cases

While some of these topics are broadly covered in the resources noted above, here are some additional resources which drill down further on specific players and uses.

For EASY summaries, the following provide a broad overview:

Additional Resources

In addition to the articles, research pieces, on-line tutorials and books noted above, here are a few more places to continue your QC education:


Classiq publishes a weekly podcast called “The Qubit Guy’s Podcast,” hosted by CMO Yuval Boger, which I highly recommend (and for an added treat, make sure to check out his February 23rd interview of yours truly).  Yuval has an excellent interview style and his podcasts are interesting and right-sized.  I particularly recommend his interviews of Jack Hidary (Google/Sandbox/Alphabet), Dr. Robert Sutor (IBM) and Paul Lipman (Coldquanta), although all are worth listening to.

Inside Quantum Technology also showcases its own podcasts, hosted by Christopher Bishop, another highly skilled and interesting interviewer.  I suggest his interviews of Chad Rigetti (Rigetti Computing) and Ilyas Khan (Quantinuum).


The Quantum Insider, led by Alex Challans and Evan Kubes, is a great starting point for general QC information and includes directories of Companies, Investors, Funding Rounds, Universities, Government Entities and Quantum Users among other details.  They also publish a quarterly and an annual report as well as daily news aggregators.

Quantum Computing Report, put out by Doug Finke, is another aggregator that includes deep dives on players (segregated by Public Companies, Private Companies, Universities, Government and Venture) and hardware scorecards.  Doug is a key intermediary in the quantum realm and he is available for a variety of consulting assistance.

Fact Based Insight, founded by David Shaw, is yet another compendium of broad QC information including links to lots of introductory content as well as a daily news aggregator and various company directories and summaries.

Inside Quantum Technology News, in addition to hosting the excellent podcasts noted above, IQT has a robust website, news aggregation and also hosts period industry events, like the upcoming Quantum Enterprise event May 10-12 in San Diego, CA. 

The Qubit Report – Because Quantum is Coming, is another news aggregator with its finger on the pulse of current events, research, cybersecurity, software, business and technology among other important QC developments.  Their regular LinkedIn posts are a great way to stay current on evolving events and announcements.

Quantum Strategy Institute, led by Brian Lenahan, has a lot of good content including position papers and links and is focused on connecting customers with its network of cross-domain experts to provide consulting services.

Linear Algebra

The bad news is that in order to really understand and appreciate the power of QC, some basic linear algebra is required. This is especially true when trying to understand how gates manipulate qubits. While you don’t need to be an expert in all aspects of linear algebra, a working understanding of vectors, scalars, dot products (and orthogonal bases), matrix addition, matrix multiplication, eigenvalues and tensor products will enable a clearer understanding of gates and quantum algorithms. The good news is there are a ton or good resources available for self-learning. Here are two particularly good ones:

3Blue1Brown: a series of video lessons with excellent graphics to help visualize the concepts. The animation engine behind the graphics is a fantastic tool.

Khan Academy: Another resource for video lessons, with accompanying practice questions.

Other Resources

Medium: is a platform for self-publishing, used by a myriad of authors and readers.  It doesn’t have any particular industry focus, although it can be searched by topics and there are many excellent articles on Quantum Computing included.  In addition, some users curate newsletters (“publications”) aggregating content from various authors. 

qBraid QuBes Course:   qBraid is focused on helping high school students, but their content is well done, intuitive and thorough, combining video lectures with coding examples.  While some topics dive in a bit beyond what might be approached by non-science users, since it is oriented towards high school students, you should be able to get through the material without a deep math or science background. Q-munity is a 501(c)(3) non-profit aiming to connect and teach young individuals about Quantum Computing.  They offer a number free and paid courses on QC and while some have some technical details, they are geared towards high school students so should be relatively approachable.

Disclosure: I have no beneficial positions in stocks discussed in this review, nor do I have any business relationship with any company mentioned in this post.  I wrote this article myself and express it as my own opinion.

If you enjoyed this post, please visit my website and enter your email to receive future posts and updates: http://quantumtech.blogRuss Fein is a venture investor with deep interests in Quantum Computing (QC).  For more of his thoughts about QC please visit the link to the left.  For more information about his firm, please visit Corporate Fuel.  Russ can be reached at

The Case for an Annual “Quantum Games” Competition

While the amount of innovation and technical advancement in the Quantum Computing (QC) realm has been incredible over the past 12 months or so, it is still very hard to quantify the power of existing QCs, to compare one QC against another, or to compare a QC against a classical computer.

Last week (February 23rd) IonQ boldly announced that their latest QC named Aria with 32 qubits, “has achieved a record 20 algorithmic qubits and has furthered its lead as the most powerful quantum computer in the industry…”  But is this actually true?  In November 2021 IBM unveiled their Eagle 127-qubit quantum processor.  Isn’t 127 a lot more than 32?  What gives here?

In this post I suggest an annual “Quantum Games” or world Olympics in order to spur innovation and friendly competition as well as collaboration.  I’ll describe this in added detail towards the end of this post, but first, let’s set out some of the parameters in QC so that these games have added context.

The Guts of Quantum Computation

While digging into all the details of the full quantum stack and the various types of algorithms being written and run on existing QCs is beyond the scope of this post, some background will be helpful in understanding the nuances involved in building, operating and measuring QCs.

The fundamental core to a QC involves qubits, which can be electrons, atoms, photons or other tiny elements.  Storing such tiny elements in a given state, then precisely manipulating and measuring them, has significant challenges including maintaining near absolute-zero temperatures and/or vacuums.  Over the past 12-months or so, QC companies have gone from creating machines with 10’s of qubits to now machines with 100’s of qubits, with many predicting that this “order of magnitude increase” can be repeated each year.    It is generally thought that we will need to implement ~1,000,000 physical qubits in order to achieve consistent quantum advantage (i.e., when QCs can surpass classical computers performing real-world applications), so if that cadence of 10x improvement per year can be maintained, quantum advantage could be achieved within 4-5 years.

However, there are many other factors in building and implementing QCs beyond simply the number of qubits.  QCs derive most of their computational advantages due to principles of Superposition and Entanglement but because qubits are so sensitive and fragile, any noise in the system threatens to sabotage the computational power via decoherence.  Therefore, in the current NISQ (noisy intermediate-scale quantum) environment a lot of the qubits are earmarked for error correction. In addition, due to a non-cloning feature of quantum mechanics, there is no “quantum RAM” and therefore some of the qubits need to be allocated to storage overhead (i.e., noting the result of a predecessor calculation in an algorithm).  Without digging into all of the technical detail, you can think of QCs needing to address all of the following:

  1. Placing all the physical qubits in an original state, including requisite cryogenics, vacuums, microwave pulses and/or laser pulses, etc.
  2. Manipulating the various qubits to establish Superposition
  3. Applying gates to the qubits to program algorithms, including entangling certain qubits
  4. Applying error correction overhead to confirm the algorithms are performing the desired calculations before decoherence
  5. Applying compiler-level logic as well as various other layers in the QC stack
  6. Measuring the readouts of the 10’s of thousands of “shots” of each algorithm run (QC algorithms are based on the variational metric because QC calculations are probabilistic and need to be repeated many times to average to the answer)
  7. Resetting the system between calculations

While the above list is not meant as an actual blueprint, it is intended to give some sense to the various activities underway in a working QC.  There are performance bottlenecks and areas for performance enhancement in each of these activities.  Let’s categorize them for ease of further discussion.

Key QC Performance Metrics

There are four core functions or parameters of performance needed to measure QC power:

  1. Scale, or the total number qubits
  2. Quality, or the ability of the qubits to implement circuits before errors enter the system
  3. Speed, or the number of circuits that can be implemented at a given time
  4. Context, of the type of calculation being measured.  Some focus on the physical system and others focus on the applications…some focus on simulations and some on optimization, etc.

[1] Based on BCG analysis which included many other competing benchmarks.  See References for link.

Here is a bit more color on each of these four proposed benchmarking strategies.

IBM: Has proposed a three-prong measurement set of metrics including the number of qubits, quantum volume (an indication of the quality of circuits and how faithfully curcuits are implemented) and speed as measured in CLOPS (circuit layer operations per second) which indicates how many circuits can run in a given time.  While this seems like a fairly straight-forward and objective set of metrics, the criticism has been that the metrics are based on a random set of gates (theory being this keeps it objective), and therefore it doesn’t factor in real-world usage.

QED-C: The US Quantum Economic Development Consortium, which was established as a result of 2018’s National Quantum Initiative Act, has developed a suite of application benchmarks targeted towards practical applications and based on executing common quantum algorithms or programs.  Given that these benchmarks were derived from industry input, this seems like a broadly validated set of measurements.

IonQ: Has proposed #AQ or algorithmic qubits as the yardstick, and has used this standard to perform apples-to-apples comparisons with other leading QC makers.  They claim that by using the series of algorithmic benchmarks developed by QED-C, they are featuring important real-world algorithms, and by taking one metric (#AQ), advocate an easy measurement to track and compare.  They claim that having an #AQ of 20 means they can execute a reference quantum circuit over 20 qubits that contains over 400 (20 x 20) entangling gate operations and expect the results to be correct with meaningful confidence.  Below is their latest announcement with the metric shown for their latest Aria machine compared to Quantinuum Model H1.1, IBM’s Falcon and Rigetti’s Aspen M-1, with the size of the rectangle outlined in pink denoting the QC “size”.

SupermarQ: Just last week released SupermarQ, another application-centric benchmarking suite for QCs.  The target applications mirror real-world problems in a variety of domains such as finance, chemistry, energy and encryption. 

While these are some useful ways to consider measuring QC performance, it is important to realize that these firms are battling over very modest performance yardsticks in the scheme of the eventual potential of QC.  If we assume a scale of 1-100 where 100 is a robust QC that consistently achieves quantum advantage, current machines are roughly in the 5-10 range now, so arguing whether a given machine is a 5 out of 100 or an 8 out of 100 is not that meaningful from a practical sense.

That said, in addition to the metrics proposed in the table above, there are other proposed benchmarking strategies including Mirror Circuits by Sandia National Labs, Quantum LINPACK by UC Berkely, and Q-Score by Atos, among others.  In fact, to provide standards against which to measure quantum computing progress and drive current research toward specific goals, DARPA announced its Quantum Benchmarking program. Its aim is to re-invent key quantum computing metrics, make those metrics testable, and estimate the required quantum and classical resources needed to reach critical performance thresholds. 

For now, my advice is to use caution when describing the power of a given Quantum Computer.  While the number of qubits is important, it is not the only important metric.  Focusing just on numbers of qubits is like assessing the performance of a high-end automobile solely by the number of cylinders in the engine.  Clearly there are many other factors that impact drivability and performance, and a similar analogy applies to QC.

So Let the Games Begin!

Given that some benchmarks favor optimization strategies, some favor simulation, some focus on contrived theoretical tasks and others try to reflect real-world applications, some are great at 2-qubit gates but not at larger entanglements, etc., it unfortunately does not look like a universally accepted standard is going to be agreed upon in the near-future.  So instead, what if there were an annual contest like a global QC decathlon?  I think it would be reasonably easy to agree on a set of measurement algorithms, similar to those proposed by QED-C.  Different entrants could compete to win the fasted correct results in several different categories of algorithms and problems, with the start-and-stop times agreed upon and a panel of experts to arbitrate any discrepancies among entrants.  Gold, Silver and Bronze medals could be awarded for each category with an overall “best in show” award to the team that wins the most individual events or achieved the highest overall score. 

I’ll nominate myself as one of the judges.  I’d certainly love a front-row seat to watch as the players competed, each driving the best of each other.  What do you think?

Disclosure: I have no beneficial positions in stocks discussed in this review, nor do I have any business relationship with any company mentioned in this post.  I wrote this article myself and express it as my own opinion.


Langione, Bobier, Krayer, Park and Kumar, “The Race to Quantum Advantage Depends on Benchmarking,” Boston Consulting Group, published February 23, 2022.

IonQ press release entitled “IonQ Aria Further Lead As World’s Most Powerful Quantum Computer”, issued February 23, 2022.

IBM Quantum breaks the 100-qubit processor barrier,” International Business Machines, November 16, 2021.

Cross, Bishop, Sheldon, Nation, Gambetta, “Validating quantum computers using randomized model circuits,” Physical Review A October 11, 2019.

Driving quantum performance: more qubits, higher Quantum Volume, and now a proper measure of speed,” International Business Machines, accessed February 27, 2022.

If you enjoyed this post, please visit my website and enter your email to receive future posts and updates: http://quantumtech.blogRuss Fein is a venture investor with deep interests in Quantum Computing (QC).  For more of his thoughts about QC please visit the link to the left.  For more information about his firm, please visit Corporate Fuel.  Russ can be reached at

At the Intersection of Quantum Computing, Artificial Intelligence and Machine Learning

There are some obvious and not-so-obvious overlaps among various “advanced computing” concepts.  Before I describe some of the inter-relationships among these concepts, it would be helpful to level-set the general definitions:

Classical Computing: is the form of data storage and analysis utilizing transistors in integrated circuits to turn switches on or off, hence storing a given computational state as a “bit”.  These circuits are coordinated into logic gates to perform various instructions such as “AND”, “OR” and “NOT” and do so in a sequential manner.  Today’s computers are increasingly fast and robust, having enjoyed Moore’s law for nearly 50 years.  However, classical computers are beginning to hit an advancement ceiling and with the ever-increasing amount of data being collected and stored, the sequential nature of classical computing analysis is leading to longer and longer processing times for large data sets.

High-Performance Computing (HPC):  is a technology that harnesses the power of supercomputers or computer clusters to solve complex problems requiring massive computation.  While aggregating computing resources can improve overall power and speed, such increases in performance are linear (i.e., classical computing based), so an increasingly large set of resources is required as the data increases.

Quantum Computing: Quantum Computers (QCs) utilize evolving new technologies which take advantage of certain features of quantum mechanics.  It uses “qubits” instead of classical computing bits and harnesses the properties of superposition, entanglement, and interference to perform calculations.  Combining these quantum properties with a broader array of logic gates, QC’s can perform calculations simultaneously (instead of sequentially) and therefore much faster than classical computers.  QCs are relatively new, and the existing devices are still not very powerful, but they are becoming more and more powerful all the time.

Artificial intelligence (AI): is intelligence demonstrated by machines, as opposed to natural intelligence displayed by animals including humans. In AI’s most basic form, computers are programmed to “mimic” human behavior using extensive data from past examples of similar behavior. AI applications include advanced web search engines (e.g., Google), recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (e.g., Siri and Alexa), self-driving cars (e.g., Tesla), etc.

Machine Learning (ML): the study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.   There are three types of machine learning: supervised learning (classification and regression), unsupervised learning (clustering and dimensionality reduction), and reinforcement (semi-supervised learning).

Big Data: refers to large, diverse sets of information that grow at ever-increasing rates. It encompasses the volume of information, the velocity or speed at which it is created and collected, and the variety or scope of the data points being covered (known as the “three v’s” of big data).  Data analysts look at the relationship between different types of data, such as demographic data and purchase history, to determine whether a correlation exists.

Quantum Machine Learning

Using these broad definitions, we can further refine this discussion to note that “Artificial Intelligence” today, is a general catch-all category for using classical computers to parse, analyze and draw conclusions.  ML and Big Data are generally considered sub-sets of AI and HPC is a general catch-all for using mainframes, supercomputers and/or parallel processing to scale the power of classical computing.  With the recent introduction of working QCs, and given that QCs operates with different processes and logic, there is an evolving field known as “Quantum Machine Learning” (QML) at the intersection of these technologies.

Over the past few years, classical ML models have shown promise in tackling challenging scientific issues, leading to advancements in image processing for cancer detection, predicting extreme weather patterns, and detecting new exoplanets, among other achievements. With recent QC advances, the development of new Quantum ML models could have a profound impact on the world’s biggest problems, leading to breakthroughs in the areas of medicine, materials, sensing, and communications.

In a milestone discovery, IBM and MIT revealed the first experimental proof that the theory of combining quantum computing and machine learning could be achieved.  They published their findings in Nature on March 13, 2019, using a two-qubit QC to demonstrate that QCs could bolster classification supervised learning.

TensorFlow, and PyTorch are leading platforms used for classical computing machine learning.  TensorFLow is an end-to-end open source platform with a comprehensive ecosystem of tools, libraries and resources that allow researchers and ML developers easily build and deploy ML powered applications.  PyTorch is also open source and has a machine learning library that specializes in tensor computations, automatic differentiation, and GPU acceleration. 

TensorFlow Quantum

Reimagining these concepts for use on a QC, Google has released open sourced TensorFlow Quantum (TFQ) which provides quantum algorithm research and ML applications within the Python framework, designed to build QML models leveraging Google’s QC system.  To build and train such models, users would do the following:

  1. Prepare a quantum dataset
  2. Evaluate a quantum neural network model
  3. Sample or average measurements
  4. Evaluate a classical neural networks model
  5. Evaluate cost functions
  6. Evaluate gradients and update parameters

Which is graphically depicted below:

A key feature of TensorFlow Quantum is the ability to simultaneously train and execute many quantum circuits. This is achieved by TensorFlow’s ability to parallelize computation across a cluster of computers, and the ability to simulate relatively large quantum circuits on multi-core computers. 


Similarly, Xanadu’s PennyLane is another open-source software framework for QML, built around the concept of quantum differentiable programming.  It integrates classical ML libraries with quantum hardware and simulators, giving users the power to train quantum circuits.  Companies such as Menten AI are using PennyLane to design novel drug molecules that can efficiently bind to a specific target of interest.  Menten AI is seeking to develop new approaches that are beyond the reach of current classical computation by integrating QC and classical machine learning techniques.

PennyLane, is integrated with Amazon Braket, a fully managed quantum computing service from Amazon Web Services (AWS). Together with Amazon Braket, it seamlessly integrates classical machine learning (ML) libraries with quantum hardware and simulators, giving users the power to train quantum algorithms in the same way they train neural networks.  Data scientists and machine learning researchers who work with TensorFlow or PyTorch on AWS will now have a way to experiment with quantum computing and see how easily it can fit into their workflows.

“Amazon Braket makes it easy for customers to experiment with quantum computing through secure, on-demand access to a variety of quantum hardware and fully managed simulators. We are delighted to be working with PennyLane to give our customers a powerful set of tools to apply proven and familiar machine learning concepts to quantum computing. Our goal is to accelerate innovation, and PennyLane on Amazon Braket makes it easy and intuitive to explore applications of hybrid quantum computing, an area of research that aims to maximize the potential of near-term quantum computing devices” said Eric Kessler, Sr. Product Manager for Amazon Braket.


While QC is still in its early stages, there are promising developments in applying QC to Artificial Intelligence/Machine Learning.  Menten AI’s use of this technology for drug discovery and Quantum Image Processing are but two examples of near-term applications.  As the amount of stored data and images continues to explode, along with the increasing adoption of voice recognition tools (i.e., Alexa, Siri, etc.) utilization of QML will be vital to enabling efficient use of these evolving tools.  I expect we’ll see many more collaborations and tools in the QML space in the next few years.

Disclosure: I have no beneficial positions in stocks discussed in this review, nor do I have any business relationship with any company mentioned in this post.  I wrote this article myself and express it as my own opinion.


Uj, Anjaii, “Quantum Machine Learning: A Smart Convergence of Two Disruptive Technologies, ” Analytics Insights, October 24, 2018

What is Quantum Machine Learning,” published by Discover Data Science, accessed February 20, 2022

Havlicek, Corcoles, Temme, Harrow, Kandela, Chow & Gambetta, “Supervised learning with quantum-enhanced feature spaces,” Nature, March 13, 2019, accessed February 20, 2022, accessed February 21, 2022

Ho, Alan and Mohseni, Masoud, “Announcing TensorFlow Quantum: An Open Source Library for Quantum Machine Learning,” Google AI Blog, March 9, 2020

Menten AI Partners with Xanadu to Develop Quantum Machine Learning for Protein-Based Drug Discovery,” PR Newswire, January 25, 2022.

If you enjoyed this post, please visit my website and enter your email to receive future posts and updates: http://quantumtech.blogRuss Fein is a venture investor with deep interests in Quantum Computing (QC).  For more of his thoughts about QC please visit the link to the left.  For more information about his firm, please visit Corporate Fuel.  Russ can be reached at

How to Invest in Quantum Computing

For some of you that have been following these posts, or others that have been learning about the power and potential of Quantum Computing, you may be wondering how to invest in this emerging opportunity.  Unfortunately, there are not many ways for individual investors to participate, although that is an evolving situation.  I will cover some of the ways you can make direct investments, some options for some indirect investments, and a few situations where publicly traded securities should be available later this year.  For this post, I will not be expressing any investment opinion but rather wanted to showcase the various avenues for making investments today (or in the near future).  I will be covering the investment strengths and weaknesses, in future posts, for some of the companies noted below.

Quantum Focused Public Companies

IonQ: Today, there is only one significant pure-play Quantum Computing company publicly traded, and that is IonQ ($IONQ), the College Park, MD based firm founded in 2015.  The Company was launched with seed funding from New Enterprise Associates, a pre-eminent venture investor, and a license to core technology from Duke University and the University of Maryland.  IonQ has built ion trap based working Quantum Computers which can be accessed directly or through cloud partnerships with Microsoft, Amazon and Google.  In October 2021, IonQ began trading on the NYSE, and as of 2/11/21 had a market capitalization of $3 billion.  The stock has had some recent gyrations, and will likely be dragged down a bit near-term as other players go public (see Rigetti and D-Wave below) and investors re-allocate some of their QC exposure from IONQ to those other firms, but this is an essential component of any long-term QC portfolio.

Rigetti: While Rigetti Computing is not quite public, they have signed a definitive agreement to merge with a SPAC called Supernova Partners Acquisition Company II ($SNII), which values Rigetti equity at approximately $1.5 billion and will provide over $450 million in cash proceeds to Rigetti.  Rigetti is another full-stack Quantum Computing provider, but they use superconducting loops for their qubits.  While the formal merger date has not been announced, a formal shareholder vote is scheduled for February 28, 2022 and the merger should be completed shortly thereafter.  Investors hoping to get in early can buy SNII today, or watch for it to trade post-merger, at which point its symbol will be RGTI.

D-Wave:  Similar to Rigetti, D-Wave has signed an agreement to merge with a SPAC, this one called DPCM Capital ($XPOA).  For this transaction, D-Wave equity is valued at $1.2 billion and it will provide $300 million in cash.  D-Wave is a different type of Quantum Computing company that offers quantum annealing as opposed to gate-based algorithms.  While annealing is less powerful than gate-based systems, it is easier to operate and scale and D-Wave has 25 customers from among the Forbes Global 2000, so is one of the current Quantum Computing companies with meaningful current revenues.  Investors hoping to get in early can buy XPOA today or watch for it to trade post-merger as QBTS.

Quantinuum: In June of 2021, after a series of successful collaborations, Cambridge Quantum Computing (CQC) reached an agreement to be acquired by Honeywell.  Honeywell merged CQC with its Honeywell Quantum Solutions (HQS) division and in November of 2021, spun out the combined businesses into a new stand-alone company called “Quantinuum” which is owned 54% by Honeywell and 46% by Cambridge Quantum Shareholders. Separately, Honeywell invested $300m in Quantinuum which has the benefit of CQC’s software and algorithm expertise combined with HQS’s hardware expertise, creating the largest full-stack dedicated quantum computer company.   Company executives have been quoted as confirming a 2022 targeted IPO, although there has been no official company announcement.  See here for a prior post showcasing Quantinuum.

PsiQuantum:  While currently private without any publicized plans to go public, PsiQuantum has been the most venture funded QC company in the US.  To date they have raised nearly $750m, most recently at a $3.15 billion post-money valuation.  While they are not in immediate need of liquidity, nor have they announced a desire to go public, the broad investor base and recent completion of a “D-Round” hint at an IPO some time in the not-too-distant future.

Among the five companies noted in this section, only one ($IONQ) is a currently traded pure-play quantum investments.  Two have committed to going public via SPAC some time this year, one has announced plans to go public but has not taken formal steps and the fifth is not necessarily going public this year, but they are worth watching for an IPO announcement in the future.  Interestingly, should all five become public, it would represent a broad bet on qubit construction (a mix of superconducting (Rigetti), ion traps (IonQ and Quantinuum) and photonics (PsiQuantum) so would enable diversification among these leading QC hardware strategies.

Exchange Traded Funds/Mutual Funds

In addition to these five pure-play companies, there are professionally managed, publicly traded funds with a focus on Quantum Computing and/or advanced computing.  Many of these have portfolios with considerable over-lap, so the best strategy here would be to select one of these funds as your “advanced computing” vehicle to provided diversified exposure to QC.

Defiance Quantum ETF:  Defiance Quantum ($QTUM) is an exchange traded fund with a portfolio of investments in advanced technology companies that operate in Quantum Computing as well as artificial intelligence and machine learning.  While not purely “quantum” the companies in its portfolio should all benefit from increasing commercialization of Quantum Computing.  The fund trades at or near its “net asset value”, in other words it is a relatively efficient way to own a diversified portfolio of about 70 companies. Holdings of QTUM include Teradata, Lockheed Martin, Airbus, HP, IBM, IonQ and others.

Fidelity Select Technology Portfolio ($SFPTX): This non-diversified fund invests primarily in equity securities, especially common stocks of companies that are engaged in offering, using, or developing products, processes, or services that will provide or will benefit significantly from technological advances and improvements. Some of the fund’s top quantum holdings include Google, Nvidia, Microsoft and Micron Technology.

Fidelity Select Software & IT Services Portfolio ($FSCSX): This non-diversified fund invests a majority of its assets in common stocks of companies engaged in research, design, production or distribution of products or processes that relate to software or information-based services.  Some of the fund’s top quantum computing holdings are Microsoft, Google and International Business Machines.

T. Rowe Price Global Technology Fund ($PRGTX): aims for long-term capital growth. This non-diversified fund invests most assets in the common stocks of companies that will generate a majority of revenues from the development, advancement and use of technology. Some of the fund’s top quantum computing positions are Alibaba, Advanced Micro Devices, Micron Technology and NXP Semiconductors.

Franklin DynaTech Fund Class A ($FKDNX): The fund invests primarily in common stocks with a focus on companies that are leaders in innovation, take advantage of new technologies, have superior management, and benefit from new industry conditions. Some of the fund’s top quantum computing investments are Google, Nvidia, Microsoft and Alibaba.

Technology Select Sector SPDR Fund ($XLK): Seeks to provide exposure to companies from technology hardware, storage, and peripherals; software; communications equipment; semiconductors and semiconductor equipment; IT services; and electronic equipment, instruments and components.  Top holdings include Apple, Microsoft, NVIDIA, Broadcom and Cisco.

Public Companies with Quantum Initiatives

None of the following publicly traded companies are pure-play quantum investments, but each has major Quantum Computing initiatives and a varying level of reliance on successful penetration of the QC market.

International Business Machines ($IBM): As a leading legacy company focused on computing hardware, IBM seems like a natural company to lead QC efforts.  In fact, they have created the IBM Q Experience which enables more than 100 customers to access IBM’s quantum resources via cloud-based access.  In addition, IBM has developed Qiskit, one of the more popular open-source quantum SDKs (software development kits).   Their latest 127-qubit Eagle quantum processor is one of the more robust QCs available and it is being utilized by major firms including Goldman Sachs, Samsung, JPMorgan Chase, ExxonMobil, and Boeing, among others.  IBM features its quantum initiatives prominently in its corporate materials, so I expect QC to be an ever-increasing part of its value.

Microsoft ($MSFT):   As a leading software company, it makes sense that MSFT would be working on quantum software.  Specifically, they have a widely used SDK called Q# (pronounced Q Sharp) and have been offering access to the quantum hardware systems offered by Honeywell, IonQ and QCI via their Azure Quantum cloud-based quantum platform.  And, via their M12 corporate venture arm, are investors in PsiQuantum.   By remaining fairly agnostic to the quantum hardware used, and by developing an open-source SDK, MSFT is well positioned to enjoy the growing usage and needs for access to QCs regardless of which hardware technologies ultimately gain the most traction.  However, despite Microsoft’s clear commitment to Quantum Computing through their Azure Quantum platform and their Q# SDK, in their latest 10-K annual report as of June 30, 2021, there is zero mentions of “quantum” or “Q#” so it may be difficult in the near term for MSFT’s quantum efforts to move their equity value.

Honeywell International ($HON)As noted above, Honeywell spun its Honeywell Quantum Solutions (HQS) division out into Quantinuum, with a stated plan to take Quantinuum public.  However, until that spinout occurs, it is possible to obtain QC exposure via a direct investment in Honeywell.  Even once Quantinuum goes public itself, it is expected that Honeywell will retain a significant ownership in Quantinuum so acquiring shares of HON now is an early way to get in on the upside possible in Quantinuum.

Alphabet ($GOOG, $GOOGL)Alphabet/Google has been a major quantum headline grabber over the past couple of years, especially after it published the breakthrough paper in Nature describing how its Sycamore quantum processor was the first QC able to achieve “quantum supremacy.”  In addition to the Sycamore claims, Google maintains a robust quantum offering, including its Cirq SDK, cloud-based QC access and various libraries of quantum resources and algorithms.  However, like other large companies included in this section, Alphabet is a huge, diversified conglomerate, so the relative contribution of QC to the broader Alphabet valuation is likely modest.

Intel ($INTL)Intel has been a leading player in computing hardware since it was founded by Gordon Moore and Robert Noyce in 1968, so they are another corporate candidate for meaningful quantum exposure.  Additionally, as “Moore’s Law” begins to bump up against physics constraints, Quantum Computing seems like a natural extension of their technology, in order to continue to produce ever more powerful computing chips.  In fact, in 2019 Intel announced Horse Ridge, a cryogenic control chip designed to speed the development of full-stack QC systems.  Intel is hoping to leverage this chip, along with its legacy expertise around interconnect technologies, to become a major player in the QC realm.

Amazon ($AMZN): Similar to Microsoft, Amazon has a broad cloud-based quantum platform within its Amazon Web Services (AWS) offering, known as Braket.  It provides access to systems from D-Wave, Rigetti and IonQ.  They also have an AWS Center for Quantum Computing in partnership with the California Institute of Technology among others.  However, Amazon is a massive business with many interests and “quantum” is not often featured in its corporate description materials nor was it mentioned in their 2020 annual report, so its overall equity exposure to QC may not be very significant.

Nvidia ($NVDA): Founded in 1999 with a focus on advanced gaming, Nvidia’s GPU’s (graphics processing units) are now also being utilized for deep learning, parallel processing and artificial intelligence, so they have become an important player in advanced computing.  A newly announced cuQuantum for quantum computing, enables large quantum circuits to be simulated dramatically faster, allowing quantum researchers to study a broader space of algorithms and applications. Developers can simulate areas such as near-term variational quantum algorithms for molecules and error correction algorithms to identify fault tolerance, as well as accelerate popular quantum simulators from Google and IBM.  Given their success in becoming significant players in advanced computing generally, it seems likely they will have success leveraging these assets in Quantum Computing.  Currently, “quantum” is a very modest focus within Nvidia’s press or shareholder reports, so it is unlikely to have a near-term major impact on its stock value, but this may be worth taking a modest, long-term position.


For those of you anxious to invest in the evolving Quantum Computing industry, there are a few publicly available options.  Some will provide a direct, pure-play investment, while others should enjoy enhanced returns based on their QC exposure.    The following table summarizes the public company investments (and stock symbols) that would provide decent portfolio exposure to Quantum Computing upside:

Those seeking meaningful investment exposure to QC should certainly maintain positions in IONQ, RGTI and QBTS and likely at least one of the funds noted.  For added exposure to a broader advanced computing portfolio that also adds QC exposure, you may consider adding some or all of MSFT, IBM, HON, GOOG, GOOGL, AMZN, INTL and/or NVDA. 

Disclosure: I maintain personal long positions in IONQ, SNII, QTUM and XLK, but do not have any business relationship with any company mentioned in this post.  I wrote this article myself and express it as my own opinion.


Nvidia Press Release, Introducing cuQuantum: Acclerating State Vector and Tensor Network-based Quantum Circuit Simulation, November, 2021.

Zacks Equity Research, 4 Funds to Shine as Quantum Computing Comes Into Play, July 8, 2021

Intel Corporation Press Release, “Intel Introduces ‘Horse Ridge’ to Enable Commercially Viable Quantum Computers,” December 9, 2019.

Taulli, Tom, InvestorPlace, “These 7 Quantum Computing Stocks Are Futuristic Buys,” June 15, 2020.

Gecgil, Tezcan, InvestorPlace, “The 7 Best Quantum Computing Stocks to Buy for February 2022,” February 4, 2022.

Hajjar, Alamira J., AI Multiple,  “33+ Public & Private Quantum Computing Stocks in 2022”, published May 5, 2021 and updated Jan 11, 2022.

If you enjoyed this post, please visit my website and enter your email to receive future posts and updates: http://quantumtech.blogRuss Fein is a venture investor with deep interests in Quantum Computing (QC).  For more of his thoughts about QC please visit the link to the left.  For more information about his firm, please visit Corporate Fuel.  Russ can be reached at

Quantum Advantage is Closer than you Think

I recently had the pleasure of speaking with Anisha Musti, a delightful and empowering 16-year-old CEO and Co-Founder of Q-munity, a 501(c)(3) non-profit that is introducing and teaching young individuals about Quantum Computing (I encourage you to check out Anisha and her project(s) at the Q-Munity website).  She hopes to expose her peers to QC so that they will consider careers in the field, or “if they learn about it from us but choose not to pursue it, at least they will be making an educated assessment.”  Anisha’s poise and wisdom belie her age.

The fact that a 16-year-old, along with a few of her friends and co-students, have established a robust and constructive free resource is one of the topics I highlight below.  But I am starting this post highlighting this conversation because it was an interesting multi-generational dialogue.  She asked a bit about my QC journey, and I began explaining my first computer courses in college (COBOL and FORTRAN) where we used “punch cards” to store and retrieve commands.  The conversation went along generally as follows:

Me: I started in computers when we still used punch cards to record the commands.

Anisha: Huh?

Me: You know, that was before we even had floppy discs.

Anisha: I have no idea what a floppy disc is.

Which certainly made me chuckle.  I reflected on the amazing advances we’ve seen in just my lifetime.  Born in the 60’s, I entered college before personal computers (or GPS or cell phones or the Internet, etc.) and have witnessed amazing technological progress ever since.  Sometimes, when I consider the power contained in my iPhone, I am awed by it and it feels like we can’t possibly need any more technological advances…I can do almost anything, virtually instantly, in the palm of my hand.

But time and technology invariably move forward.  And in fact, we appear to be on the cusp of even more profound technological capabilities in the form of working, powerful Quantum Computers.  Using the growth in power and capacity of some electronics over the past 20 years, the following table provides a level of growth-speed context:

You may notice that the growth-rate of the speed of the processor of your PC, while substantial at 4.3x, is a tiny fraction of the rate of growth in cellular data speeds.  This is a nuance of these sorts of growth rates, which are more explosive earlier in the life cycle, but eventually slow down and physical limits become more difficult to overcome.  There is also a relative utility factor, in that PC’s created in 2002 were pretty good at basic office program usage (email, word documents, spreadsheets, etc.) so the utility of speed increases was less valuable.  Compare that to gaming consoles.  While the graphics of Grand Theft Auto: Vice City (#2 videogame of 2002) may have made your mother cringe, it is a far cry from the realism experienced by today’s FIFA22.  In other words, the consumer utility of increased speeds and capacity is still a steep demand-curve for certain technologies, especially for those with substantial headroom in progress and need.

Given the utility of improved Quantum Computing, it is my opinion that the rate of growth will continue to accelerate at a phenomenal rate.  We are already seeing 10x/year increases in quantum volume (albeit over a short window of time) and I expect that pace to remain or accelerate in the near term, as I’ll explain below.  While there has been much written on this topic, and many billions of dollars invested, many still speak of a “quantum winter” where the hype overshoots the reality.  Readers of my posts know that I am mindful not to contribute to the hype, but I truly believe that useful, practical Quantum Computing applications are imminent (i.e., by the end of this decade or sooner).  Let me explain a few reasons why.

  1. The Quantum Evolution is Quite Mature

In 1879, electricity was first harnessed for home use to power Edison’s electric light bulbs.  During the period of 1920-1935 the US went on an electrification campaign bringing power to 70% of US homes.  So, in about 50 years, a profound new technology became ubiquitous.  Nobody could have imagined the impact electricity would have on daily life in those early years.  Yet today we take for granted that we can plug a cord into any wall in our home and have instant, nearly free power.  Personal Computers and the Internet have had similar, profound impacts on our daily lives, generally over shorter and shorter spans of time.

Quantum Computing has the potential to be a next profound disruptor.  Many authors, including me, have covered the power and potential of QC, so that is not the focus of this post.  Rather, the concept to keep in mind, is that while “Quantum Computing” is relatively new, the utilization of quantum physics/mechanics has been progressing for the past 130 years.  We have had great success utilizing the dual wave-particle nature of electrons and photons for a variety of purposes including MRI’s, lasers and GPS (which I covered in a prior post entitled “Quantum Quantum Everywhere”), among many others.  As that prior post noted, today we are already using quantum mechanics in Quantum Sensing for precise measurement probes (even where GPS is unavailable), ghost imaging and quantum illumination.  It is also being used today for certain applications of Quantum Communication.  And yes, while current Quantum Computers are not as powerful as we’d like, there are dozens of companies offering access to their working Quantum Computers today, with the power of the machines increasing quite rapidly.  While it is difficult to get consensus over exactly when the QC’s will become powerful enough to surpass classical computers for real-world problems, nearly everyone in the field will confirm it is just a matter of “when” not a matter of “if”.

  1. Cutting edge Quantum Processors are Available in the Cloud

As noted above in in a prior post, there are a variety of QC companies offering their latest QCs via cloud-based access.  This is important because it “socializes” access to QCs.  Today, anybody with some basic computing chops, can access actual, working QCs for modest, or in some cases, no cost.  Quantum algorithms are being written and run every day.  Furthermore, because many QC makers are providing their latest QCs via the Cloud, commercial users do not have to deal with a large CapEx (capital expenditure) cost up-front nor do they have to worry about obsolescence.  When mainframe computers became available to commercial users in the early part of the 21st century, they were extremely expensive, difficult to operate, and subject to being outdated relatively quickly.  The same was generally true of desktop computers, which often were made obsolete due to advanced software, well before they stopped “working.”

By utilizing QCs over the cloud, this cycle of CapEx àObsolescence à CapEx can be eliminated, which should spur greater utilization and adoption of QC than otherwise might occur.

  1. Open Source is the Default

I mean this in a broader sense than you might expect.  On the one hand, most of the existing QDKs (quantum development kits) are both open source (i.e., free to use) and cross-platform compatible.  What this means from a practical perspective, is that the learning curve for QC proficiency is much less steep because whatever skills are acquired can be used across many different platforms.  In addition, someone who creates a QC algorithm to access via a cloud provider such as Amazon’s Braket or Microsoft’s Azure Quantum can have the same algorithm run over a variety of QC hardware provider platforms.  Contrast this with early PC access where PCs did not speak to Mac’s or Linux boxes.  In addition, they required competing software, input devices and physical plugs in many cases.  All of that “confusion” made it difficult for the industry to scale at the same pace it might have, if all power users spoke the same language and used fully compatible hardware.

Even more profound and telling in the current QC environment is the “open” nature of so many of the participants.  Access to the programs offered by Anisha’s Q-Munity, noted in the opening paragraph, is free.  Many authors have published complete textbooks on Quantum Computing (Thomas G. Wong’s Introduction to Classical and Quantum Computing, and Brian Siegelwax’s Dungeons-n-Qubits are but two examples) for free.  And there are innumerable fist-rate on-line courses and programs about Quantum Computing for free.  In addition to all the free resources, I have found that the players and participants in the industry are also generally open, friendly, and eager to help folks on their quantum journeys.  This spirit of community and cooperation is refreshing, especially around an industry with such tremendous commercial potential.  Perhaps this openness will be less pervasive once the industry gets more mature (and companies are competing more vigorously for QC customers), but the essence of this post is to suggest that point arrives quickly, and this current state of openness certainly accelerates access to, and development of, quantum technologies.

  1. QC is Leveraging Adjacent Technologies

In addition to leveraging the historical progress in taming quantum mechanics for commercial use, recent advances in machine learning, artificial intelligence and big data are quite complementary to Quantum Computing.  Many advances and breakthroughs in these industries can be accelerated or improved by applying QC technology, so the pool of well experienced, advanced computing talent, is quite larger already, even in the relatively early stages of QC evolution.  Similarly, we see certain quantum hardware strategies leveraging existing advances in semiconductor technology (i.e., quantum dots) and optics (photonic qubits) to create QCs.  As the hardware advances and applications continue to evolve, I expect many to also converge.

  1. Quantum Advantage is a Continuum not a Milestone

As a refresher, while there is no definitive guide to definitions about QC, “quantum supremacy” is generally referred to as a QC being able to tackle a problem, even one without real-life application, faster than a classical computer.  This was achieved in by Google in 2019 and repeated by others since.   “Quantum advantage” on the other hand, is meant to denote when QCs can out-perform classical computers in actual, useful applications.  The QC world is anxiously awaiting this Quantum Advantage threshold without clear consensus on when that might occur.  However, as those who study quantum effects well know, things are never so binary!  It is more constructive to think about QC progress as a continuum, not a specific threshold to be achieved.

I am not the first to suggest this perspective.  In a recent Harvard Business Review podcast, host Azeem Azhar interviewed Rigetti Computing founder and CEO Chad Rigetti.  In it, Rigetti noted select instances where a QC offered a very slight performance advantage to a small part of a broader problem.  He discusses how this happened with Rigetti’s attempt to improve weather forecasting.  While this is certainly not Quantum Advantage, it is a real-world example, today, of QC contributing to real analysis.  Chad elaborated on some of his thoughts around “narrow” versus “broad” quantum advantage, which I found very compelling.  Specifically, he referred to “narrow advantage” where a specific use case might benefit from QC, such as in the pricing of derivatives.  Any small advantage could produce outsized financial benefits in portfolio allocation or timing of trades, and could occur well before “broad advantage” is achieved.  While financial markets are just one example, the finance industry is already very computing advanced and the underlying data is already in computing format, so this sort of narrow quantum advantage could be quite close.  Broader quantum advantage, where QCs can generally outperform classical computers, is more difficult and therefore further away, but I imagine we will see many steps up a spectrum of advances on the way to full quantum advantage.

  1. Calling Dr. Evil…

The final point I want to make, is the enormous economic impact that a powerful QC will make.  The heading of this section, using a tongue-in-cheek reference to Austin Powers movies, is meant to evoke the massive commercial gains that can be made with a powerful QC.  Much has been written about using Shor’s algorithm to break current encryption protocols and the “HNDL” (hack now, decrypt later) movement, which unfortunately is a real thing.  A bad actor or nation-state could command enormous power if they were the first to create a powerful QC.  They could break most encryption, mine all remaining bitcoin and other cryptocurrency and skim untold profits from financial systems by front-running traders, just to name a few powers.  In fact, the US and China are currently engaged in a ferocious race to develop the most powerful QC capabilities, each fearing the other’s ability to get there first with each establishing nationally supported quantum initiatives.

Naturally I hope and expect that the “good guys” will have the most powerful QCs and will focus their powers on good use such as better medicines, more efficient car batteries and optimized logistics, among other things.  Certainly, the rewards and upside for constructive use of QCs is enormous, and smart people are busy at work to protect us from those bad actors.  The point is that the massive financial upside for access to powerful QCs will spur accelerated development.

So can we say with any certainty, what a QC timeline looks like?  Unfortunately, not.  But as this post points out, the foot is on the accelerator, billions of dollars are being invested and super smart people are working on creative solutions to existing progress rate delimiters.  For these reasons and those enumerated above, I am confident that we will begin seeing quantum advantage in our daily lives more and more over the next few years.

Disclosure: I have no beneficial positions in stocks discussed in this review, nor do I have any business relationship with any company mentioned in this post.  I wrote this article myself and express it as my own opinion.


Azhar, Azeem, Host, “How Quantum Computing Will Change Everything (with Chad Rigetti)”, Season 6, Episode 11, Harvard Business Review, December 2021.

Intel® Microprocessor Quick Reference Guide – Year, accessed February 5, 2022

The astounding evolution of the hard drive (, accessed February 5, 2022             

A Brief and Abbreviated History of Gaming Storage – Techbytes (, accessed February 5, 2022

If you enjoyed this post, please visit my website and enter your email to receive future posts and updates: http://quantumtech.blogRuss Fein is a venture investor with deep interests in Quantum Computing (QC).  For more of his thoughts about QC please visit the link to the left.  For more information about his firm, please visit Corporate Fuel.  Russ can be reached at

Cloud Based Quantum Computer Access – Available Today

If you have been following this blog, hopefully you have some broad appreciation for the promise and potential of Quantum Computing (QC).  This is a rapidly evolving field and generally, the hype has been front-running the actual capabilities.  While a narrow “quantum supremacy” has been achieved by Google and others, general “quantum advantage” (where a quantum computer can out-perform a classical computer for a given real-world problem) is still out of reach (for now). 

That said, the purpose of this post is to highlight and showcase the fact that people are using actual working Quantum Computers every day.  Each of Amazon and Microsoft offer cloud access to several QC hardware systems, while players like Google, IBM, IonQ, Rigetti, Honeywell and others offer direct access to their systems via direct web-based interfaces.  I’m going to spell out some of the modes of access these firms have made available, not to be a definitive catalogue of all QCaaS (Quantum Computing as a Service) providers but to emphasize two facts:

  1. Many people and companies are already using quantum computers to process real-world quantum algorithms.  Results are generally less robust than can be achieved on existing classical computers, but routines are being run and occasionally, results surpass classical computing results.
  2. The industry is moving towards a largely open-source software environment for programming and accessing quantum processors. Many quantum hardware manufacturers are offering cloud-based access to their systems, obviating the need to purchase physical quantum hardware.  This substantially lowers the barriers to entry for companies seeking to begin exploring how QC can benefit their businesses, and “future-proofs” the investment since the providers continually upgrade the QC machines they provide via the cloud.

Working QCs are currently available to anyone (and as you’ll see below, the costs of operating QCs can be quite modest).  In fact, a recent study reviewed all of the QC cloud access of IBM’s Quantum systems over a two-year period and found over 6,000 jobs which contained over 600,000 quantum circuit executions and almost 10 billion “shots” (a shot is a single execution of a quantum algorithm on a QPU (quantum processing unit), further described below).  IBM notes on their website that they have run over 1 trillion circuits to date, which is clearly a non-trivial amount.  And that is just at IBM. 

The following tables highlight some aspects of the current state-of-play in using actual quantum computers to run algorithms via cloud-based access:

Note : Other providers include Alibaba Quantum Lab (China), Alpine Quantum Technologies (Austria), Origin Quantum (China)

[1] Quantum annealing is a different protocol than typical Quantum Computing gates so is not a direct equivalent when comparing numbers of qubits.

Quantum Computing Power Available Via the Cloud Today

Before I get into details about specific methods for accessing working Quantum Computers, I want to review a few facts about the state of the industry vis-à-vis QC power.  The current environment has been referenced as “NISQ” or noisy intermediate-scale quantum.  Generally, this means that existing quantum computers operate with a lot of noise that interferes with qubit control and coherence, and that working quantum computers have a somewhat limited number of qubits.  QC power can be increased both with the addition of more qubits and/or with the successful implementation of error-correction.  Generally, a QC with about 50-60 working logical qubits (representing around a petabyte of processing power) should begin to achieve consistent quantum advantage.  Some expect this will require as many as 1,000x more qubits per logical qubit to handle the error-correcting overhead, although as control and error-correction improves, this number should decrease.  In any case, today’s working QCs provide 10’s of working qubits not 100’s or 1000’s, but they are working, accessible machines, nonetheless and beginning to yield significant computing power.

To emphasize how existing Quantum Computers are already showing real-world promise, Rigetti Computing recently used their machines to augment a portion of GSWR (Global Synthetic Weather Radar) analysis using their 32 qubit QC, and in select instances, were able to modestly outperform results achieved using only classical computing power.  Similar select improvements over classical computing have also been noted in certain portfolio/security valuation algorithms.  So real-word real benefits are beginning to appear even in this early NISQ environment.

What does it Cost to run Quantum Algorithms via the Cloud?

In order to provide an example of how you can begin accessing Quantum Computers and running quantum algorithms, the following describes access via Amazon Braket:

You can use an account with Amazon Braket to access the Quantum Computers provided by IonQ, Rigetti or D-Wave.  Once you construct a quantum algorithm, it is recommended that you test and debug it on a simulator, which is generally available for no cost.   Once you are ready to actually run the algorithm on a bona fide quantum machine, there are some cost factors to keep in mind.  There are generally two pricing components when using a quantum computer or quantum processing unit (QPU) via the cloud: a “per-shot” fee and a “per task” fee.

As you may recall from prior posts, quantum algorithms are “probabilistic” not deterministic.  There is no single correct result from a quantum operation, rather outputs are aggregated and averaged to determine the correct output.   For this reason, algorithms are usually run many, many times (10,000 times is a standard number).  A “shot” is a single execution of a quantum algorithm on a QPU. For example, a shot is a single pass through each stage of a complete quantum circuit on a gate-based QPU.  The per-shot pricing depends on the type of QPU used but is not affected by the number or type of gates used in a quantum circuit or the number of variables used in a quantum annealing problem.

A task is a sequence of repeated shots based on the same circuit design or annealing problem. You define how many shots you want included in a task when you submit the task to Amazon Braket.  The current pricing to run algorithms via Amazon Braket are as follows:

  • D-Wave 2000Q: $0.30/task  + $0.00019/shot
  • D-Wave Advantage: $0.30/task + $0.00019/shot
  • IonQ: $0.30/task + $0.01/shot
  • Rigetti: $0.30/task + $0.00035/shot

For example, a scientist runs a quantum algorithm on the Rigetti Aspen-11 quantum computer in the AWS US West (N. California) Region. This task includes 10,000 repeated shots of the same circuit design. The cost to run this task includes a per-task charge of $0.30, plus 10,000 shots at a per-shot price of $0.00035.

So, the cost to run this algorithm:
Task charges: 1 task x $0.30 / task = $0.30
Shots charges: 10,000 shots x $0.00035 / shot = $3.50
Total charges: $3.80

Competing quantum cloud providers have similar pricing constructs or charge a fixed amount for a certain level of access/time.  Naturally there is no guarantee that your circuit or algorithm will provide the desired results, or useful results, but the table stakes to begin testing QC for your business is quite modest.  These costs may increase in the future, but it is a very low bar considering the potential upside, and certainly less expensive (or risky) than purchasing a dedicated Quantum Computing machine today.


By providing access to actual Quantum Computers via the cloud, a handful of QC hardware makers are providing QC access to virtually anyone.  With a basic working knowledge of Python (a common programming language, particularly good at connecting various components), a user can investigate many free open-source resources and QDKs (quantum development kits), begin compiling “quantum algorithms” and test/debug them for free on any number of cloud-based simulators.  Once ready to run on an actual, working Quantum Computer, you can then sign up directly at some QC providers or via Amazon or Microsoft’s web platforms (or others) and have the algorithm run on an actual QC for a very modest cost.  Such access eliminates the capital and technology risks of purchasing a Quantum Computer.

This is already happening with, literally, trillions of circuits run to-date.  While the power of the machines currently accessible are modest relative to high-performance classical computers, real-world achievements are becoming increasing possible.  As more users are provided with broader access to ever larger QCs, and as advances in error correction and control continue, it is only a matter of time before consistent quantum advantage is available to nearly anyone.


Enos, Graham; Reagor, Matthew; Henderson, Maxwell; Young, Christina; Horton, Kyle; Birch, Mandy and Rigettin, Chad; Synthetic Weather Radar Using Hybrid Quantum-Classical Machine Learning, November, 30, 2021

The Quantum Insider, QCaaS write-up, accessed January 26, 2022

Dilmegani, Cem, Quantum Software Development Kits in 2022,, accessed January 22, 2022

Ravi, Gokul Subramanian; Smith, Kaitlin; Gokhale, Pranav; Chong, Frederic, Quantum Computing in the Cloud: Analyzing job and machine characteristics, University of Chicago papers, November 1, 2021

Shaw, David, “Quantum Software Outlook 2022”, Fact Based Insight, January 19, 2022

If you enjoyed this post, please visit my website and enter your email to receive future posts and updates: http://quantumtech.blogRuss Fein is a venture investor with deep interests in Quantum Computing (QC).  For more of his thoughts about QC please visit the link to the left.  For more information about his firm, please visit Corporate Fuel.  Russ can be reached at