Imagine that your great-great-great…. great grandfather was alive at the beginning of the modern era. Alive at 0 BCE. Imagine he invested one penny in an account that would earn just 2% per year. Can you guess how much that account would be worth today?
That penny, compounding at only 2% per year for 2,022 years would we worth $2.4 TRILLION dollars today. That measly penny. This is the power of compounded interest, which is a function that grows exponentially, and I point out to emphasize how powerful exponential growth can be. Quantum Computers (QCs) have the power to provide exponential speedup in processing power.
Exponential math is difficult to appreciate so I hope the penny analogy helps provide some context. I’m not going to explain the math or the underlying QC principals that govern this power, so for the sake of this post, try and just appreciate the fact that Quantum Computers are exponentially more powerful than existing computers. (For those interested in more of the details, please see a prior post on superposition and entanglement which you can read here).
Information is Power
If we think about the advances of humankind, the societies with the most advanced technology generally out-lived or out-powered their neighboring societies. Survival of the fittest to some extent. Unfortunately, this has manifested in some barbaric says, such as with the settlers to the North American continent and those who created the slave trade by overpowering African societies. The guns and weaponry of those explorers/exploiters simply overpowered the native people. Mankind has a history of using its resources to its advantage and of one society enslaving another. This is not a pollical post and I am not intending to condemn any person or peoples (although please be clear I am not glorifying them either), I am simply pointing out that human nature has shown that societies will leverage their technological superiority in self-interested ways.
Information is Power and Power is Money
Some of you may be familiar with the acronym “HNDL”, which stands for “Hack/Harvest Now, Decrypt Later.” It is also sometimes referred to as the Y2Q challenge because it is describing that in the not-to-distant future (some say 2-3 years, some say 5-10 years) Quantum Computers will be able to break RSA encryption. RSA encryption is the de-facto world security standard that is used continuously to make on-line interactions and payments secure. It is based on mathematics that are too difficult for classical computers, to provide “keys” or encryption codes to secure digital assets and transmissions. Imagine for a minute that you are the first to build the QC that can break RSA. You would have the power to read nearly any secret message sent over the Internet and could hack into an untold number of digital wallets and financial accounts. Imagine the power and wealth you would have with such a machine. Bad actors are accumulating encrypted information now, with he intent of holding it until they have access to powerful enough QCs to break the encryption as soon as they are able. It’s not a matter of if, it’s a matter of when. In fact, the algorithm to break RSA is already written! It is known as Shor’s Algorithm and it is open sourced and available to anyone from any country (don’t take my word for it, you can find it here). All that is required is a powerful enough QC to run the code. The race to build that machine is on.
There is a reason China has dedicated over $10 billion to its national Quantum Computing efforts. If China or some other rogue nation or group gets access to a powerful enough Quantum Computer, you can imagine what self-interested things they might do with it. Hack all the cryptocurrency wallets, mine all the remaining bitcoin, steal money from financial accounts, steal competitor secrets, etc. There is a massive global race to create more and more powerful Quantum Computers (see the “Follow the Money” post here for more details). And while much of this post has been focused on the nefarious uses some might apply with their new QC, there are also enormously positive things that can be achieved. New medicines, more efficient solar panels and rechargeable batteries, cheaper fertilizers, more efficient logistics, plus the many, many new applications that this new computing power will enable. For good or for bad, Quantum Computers will lead to immense wealth creation and concentration.
The Quantum Computing “Manhattan Project”
The US’s efforts to build the atomic bomb during WW II became known as “The Manhattan Project.” The famous E=MC2 equation indicated the massive amount of energy that could be released from a relatively small amount of matter (you will note that exponential power again with “C” or the speed of light being squared). The world powers during World War II were in a desperate and furious race to create an atomic bomb first. It was a national imperative and substantial resources were directed at the effort. The bomb was theoretically possible, but at the time, there was no clear path to an actual atomic weapon. However, its potential was well understood, and the underlying science and technology kept pushing the envelope. It was an existential threat. It was urgent. If the Nazi’s had gotten there first (and they very nearly did), I expect we would be living in a very different society today. The power and potential for QCs is well established based on the power of quantum superposition, entanglement, and related quantum effects. The exponential speedup can already be calculated, now it is a matter of time before the machines achieve the massive speedup that is possible.
Quantum Computing is the existential threat of today. This is not hyperbole or some doomsday prediction. The power of Quantum Computing is undeniable. Having the most information and/or the most powerful processer of that information will create enormous competitive advantage. China is pushing hard to make QCs. They already have a quantum satellite, “Micius,” orbiting the planet, which has successfully demonstrated inter-continental secure quantum transmission. The power of Quantum Computing is well known and the physics of creating working machines is evolving rapidly. Actual, working Quantum Computers exist today, albeit they are not yet more powerful than classical computers. They need to scale up, which is challenging but doable, and substantive progress is being made rapidly. There is ample motivation and over $25 billion in global investment in the space. Somebody will get there first. It will happen in this decade.
What does this mean?
It means that the world is on a path to quantum advantage. The estimated timelines to the arrival of practical, useful QCs even in the NISQ era (the N stands for “noise”), are getting shorter not longer. Advances are being made continually. It will not be a straight line. I am certain that the vagaries of the markets will cause capital flow and valuations to be spikey. And many exciting new quantum companies will not survive. Those who invest in the winners will enjoy significant outsized financial gains, and those who back the companies that do not survive, will lick their wounds. But as an industry, the race is on, the potential rewards are massive, and the clock is ticking. I sure hope that whoever wins the race uses the power to do good in the world. Stay tuned to this blog to follow the winners and losers.
If you enjoyed this post, please visit my website and enter your email to receive future posts and updates: http://quantumtech.blog
Russ 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 russ@quantumtech.blog.
The Quantum Computing (QC) industry is quite new yet is growing enormously fast as people, companies and countries around the world dedicate considerable time and resources to the evolving field. At its core, QC is centered around the exquisite control of individual atoms or photons. This typically requires conditions including extreme cryogenics, complete vacuums, precise laser/microwave control, ultra-sensitive sensors and brand-new computing languages. It is remarkably difficult, yet progress is being announced daily and working QCs are in use today (albeit not very powerful ones).
And because the underlying quantum mechanics happen on a scale we cannot see, feel or touch at our “people” scale, and involves difficult mathematics including things like “complex” and “imaginary” numbers, it’s reasonable to expect many people to be highly skeptical. Just try and explain superposition, entanglement or observer effect to someone not exposed to those concepts before, and you’ll know exactly what I mean.
That said, a lack of understanding of something does not mean it is a hoax.
Yesterday an activist short-selling firm named Scorpion Capital published a 183-page report excoriating every aspect of IonQ (and QC generally). The stock closed at $7.15 per share, a drop of 9% for the day on heavy trading volume and down 57% year-to-date. Market capitalization has fallen by $1.7 billion so far this year.
The Scorpion report is 183 pages, contains lots of charts, purported “extensive” interviews with insiders and plenty of smart-seeming graphics and quantum descriptors. It uses this presentation to claim that IonQ is a hoax. So it must be legit, right?
Wrong!
I won’t get into all the details and won’t include a link to the report because I don’t want to contribute to perpetuating its false claims (you can easily find it on the web if you’re curious). Here are just a few quick examples to support my position that the report is a ruse meant to lead to a decline in price in order to enrich the author.
It’s a poorly constructed, cobbled together report meant to present its heft as validation of its conclusions. A friend who pointed it out to me said “it’s so long it must contain some truth.” That was the author’s intent. The report contains a large number of differing fonts. This is not a professionally prepared, exhaustive analysis, it is a big cut-and-past exercise. I’d bet it was produced largely by a bot.
The author runs a hedge fund focused on short positions (i.e., borrows stock which it then sells, hoping to buy it back when the price declines). It is in the author’s immediate self-interest to cause a panic that leads to a steep and fast stock price decline. Readers should be clear it is a self-serving piece.
It contains some true quantum references such as superposition, and it cites generally known current limitations in QC including the requirement of substantially more qubits to have meaningfully useful QCs. This is used to imply that the 11-qubit and 32-qubit machines IonQ offers are “toys”.
It notes that IonQ uses “trapped ions” for its qubits, and that “ion traps” were invented in the 1950’s suggesting, therefore, that IonQ hasn’t done anything new. It refers to their machine as a “Willy Wonka or steampunk factory” because of all the “lasers and Nikon-like lenses.” Yet somehow IonQ is able to run a Hamiltonian Simulation and Bernstein-Vazirani algorithm, among other quantum application-oriented benchmarks, which is conveniently ignored by Scorpion.
This is a repurposed report of a similar nature published by Scorpion in April 2021 (that one is 188 pages) to do a comparable hit job on Quantumscape, a battery company. It’s a replay of many of the same sham allegations where the author exaggerates claims about the SPAC structure, with similar references to Tharanos, etc. to scare readers into selling the stock. There has been plenty of press coverage refuting that prior report and a growing chorus today of others besides me calling Scorpion to task for its misleading claims about IonQ.
The report claims things like “fictitious revenue” and “sham transactions.” And suggests throughout that the company is a scam. Tell that to the underwriters (and their lawyers) who did extensive diligence. Or Ernst & Young who delivered a clean audit opinion. Or Amazon, NEA, Google and Breakthrough Ventures who all invested substantial sums in the company. Or Microsoft, Amazon and Google who all make the IonQ machines available via their quantum cloud services. Or Accenture, Goldman Sachs, and Hyundai who are among a growing list of cash-paying customers. Or to the many people running algorithms on the IonQ machines available via cloud access.
I’m confident that the diligence done by all of the firms noted above was substantially more robust than that done by whatever bot created the Scorpion report. Again, I do not want to provide oxygen to the report so won’t get into further details here. I hope that you will do your own analysis before relying on information like that report to make investment decisions.
So why am I writing about this? For two reasons: 1) To highlight the fact that some QC companies are now public, with billions of dollars of market capitalization in the balance. Strong moves up or down by these early public QC companies will have powerful implications for the many others that hope to use public markets to help fund the steep needs required to advance this bleeding-edge science; and 2) The concept of a “quantum winter” is a real fear in the industry and a challenge that has befallen other bleeding-edge technologies (e.g., nuclear fusion). Those of us reporting on the QC industry need to be rational and sober when describing the timeline for quantum advantage and try not to over-promise. Hype has a tendency to rebound strongly in the other direction, so I caution all QC advocates to temper their claims and enthusiasm so as to not over-hype achievements and advances. As with many things, it’s better to under-promise and over-deliver.
So, is quantum winter coming? Not in this writer’s opinion. There is still a long way to go before we have QCs that can add value to computational problems and even further before consistent quantum advantage. The hill is steep and the climb is arduous, but I am confident the view will be worth the climb.
References:
IonQ website and presentations, accessed May 3, 2022.
Kahlon, Kir, “The ‘World’s Most Powerful Quantum Computer” is a Hoax With Staged Nikola-Style Photos – An Absurd VC Pump with a Recent Lock-Up Expiration Takes SPAC Abuses to New Extremes”, Scorpion Capital, May 3, 2022.
Disclosure: The author maintains a personal long position in IONQ but does not 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.
If you enjoyed this post, please visit my website and enter your email to receive future posts and updates: http://quantumtech.blog
Russ 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 russ@quantumtech.blog.
A prior post entitled “Collaboration Dominates Quantum Computing” included an overview of Classiq, a quantum algorithm design company. This post will include additional details about Classiq along with an overall assessment of the likelihood of their success which is summarized with a RatingAlpha = 0.95/Exceptional Performance Expected (see the Rating section for details).
Background
Classiq (or the Company) is a quantum algorithm design company founded in 2020 and based in Tel Aviv, Israel with offices in the US. The Company is 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. As Yuval Boger, Classiq’s Chief Marketing Officer told me “This is a new … and better way to create quantum software.”
The Classiq Platform
In these early days of Quantum Computing (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 QCs 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 in an article entitled “Why Is It So Difficult to Write Software for Quantum Computers? And What Can Be Done About It”). 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 their own intellectual property (IP) without needing to risk IP leakage to external providers. As companies build proprietary quantum software applications, enabling this IP protection will be essential.
Classiq provides software that automatically transforms high-level functional models into optimized quantum circuits for a wide range of back-end systems. Their goal is to allow algorithm designers to write high level functional models of their algorithm and have the Classiq software automatically compile it to an optimized gate level implementation.
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. The QAD automatically synthesizes complete quantum circuits from high-level functional models in seconds and does so in a clean and elegant 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, QIR and others.
Instead of expressing quantum circuits using a series of gate-level or building block connections, algorithm designers that use the Classiq platform write functional models, similar to the very successful approach used today in designing sophisticated computer chips. The Classiq QAD platform then examines the full implementation space to find an outcome that fits resource considerations (e.g., circuit depth), designer-supplied constraints (e.g., overall accuracy) and the target hardware platform (e.g., qubit count). While it may not be obvious that circuit “optimization” is crucial for a QC with only a handful of qubits, as Amir Naveh, Classiq co-founder and Head of Algorithms noted in a recent interview “we’re not talking about twofold optimization. We’re talking about a thousandfold optimization”, enabling smaller machines to achieve more powerful results. And once there are larger machines with better accuracies and fidelities, “you can go ahead and run even more complex problems. You can expand the envelope of what you’re allowing your [quantum] computer to do.” In essence, the Classiq platform may seem like added overhead if working with a QC with only a modest number of qubits, but it will establish practices and disciples to optimize machine capabilities, on whatever QCs are available, and provide a strong advantage over competitors that don’t take advantage of the Classiq platform.
Once the various inputs and constraints are entered, the Classiq QAD will generate an algorithm that might look a bit like this:
You can see the various qubits listed along the y-axis and the various functional blocks that comprise the algorithm. If users want to drill-down to see what is within a given block (such as the block above circled in red), they simply click the “+” sign and it is further broken out visually as shown below:
Which, in turn, can be further highlighted and drilled down to the following:
The user interface is distinctive and intuitive, and the drill-down capabilities facilitate editing and debugging. It also has the added benefit of being easy to follow and utilize even without knowing much about QCs. Today, most quantum algorithm design requires PhD-level knowledge of quantum information science (QIS). This is challenging because there are not many people with this level of knowledge, and because it is even harder to find QIS experts that also understand finance, supply chain, chemistry, or whatever business problem you want to solve. Classiq puts the ‘quantum expert in a box’, allowing customer personnel with expertise in areas outside quantum to also make meaningful contributions to the quantum efforts.
Software-as-a-Service Model
Classiq’s business model is principally one of software-as-a-service (SaaS), where they offer seat licenses to end users for access to circuit conversion software via cloud access. Generally, customers download a front end such as a Visual Design Studio plug-in or Python SDK, to work out their broad coding parameters and then upload it to the Classiq provided interface for the actual algorithm synthesis. This model provides the typical benefits of SaaS including always having access to the most current version (i.e., no obsolescence cycle) without concerns regarding speed, capacity or backups/platform maintenance related to the Classiq solution. In addition, as Classiq develops various upgrades and learning packages, those are automatically available via the SaaS model. The current version has various finance, optimization and chemistry packages, with a machine learning package in development, which will automatically be available once released.
A recently released software version makes it easy for customers to add their own functionality on top of Classiq’s rich set of existing functional models while leveraging the circuit synthesis and optimization capabilities of the Classiq platform. This means that instead of writing programs from scratch, Classiq users can now leverage functional models and knowledge bases created by internal domain experts, external providers, or Classiq itself. Third parties can now also create add-on packages for the Classiq platform and use those to market their unique quantum expertise. This new capability allows users to include their own unique intellectual property (IP) and custom functional models. As an example, the Classiq engine provides gaussian and log normal probability distributions as standard functions for use in things like Monte Carlo simulations, but if a user wanted to use a different probability distribution function, such as a Pareto distribution, they could define such a functional block in the Classiq platform.
Classiq Wants to Teach you How to Fish
There are other companies offering ways to simplify writing quantum algorithms, but Classiq is unique in its approach. In these early days of NISQ, certain quantum software firms offer “off the shelf” quantum primitives or pre-packaged algorithms. There are a number of problems that can be reduced to simple starting points called primitives, which allow users to run code for specific tasks. For example, Qiskit, the software development kit (SDK) put out by IBM and generally considered to be the most popular QC programming tool/platform, offers a number of primitives including “Sampler” and “Estimator” which perform tasks as their names imply. In fact, there are a handful of primitives today which run the gamut of most of the known and hypothesized use cases for QC. In addition to Qiskit’s “Sampler” and “Estimator”, other common primitives include various variational quantum algorithms (VGAs) such as QAOA, VQE and others (see github or Quantum Algorithm Zoo). There are also various intermediaries (i.e., quantum consultants) and quantum software providers that can customize such primitives for specific use cases. In fact, as noted above, Classiq offers a number of core algorithms within its platform, which users can use as provided. Therefore, you may be thinking that the industry already covers quantum algorithm synthesis, and you can hire firms to customize them if needed. So why the need for a QAD platform to build them or customize them? There are three core reasons why this is important:
Having tools to customize algorithms internally allows customers to build their own quantum software IP. This is a subtle but crucial difference.
Algorithms developed for one quantum platform, may not perform as needed on an alternative platform. QAD enables users to optimize and customize performance depending on the hardware utilized. As Amir Naveh further noted: “Maybe you built an algorithm optimized for your specific circuit and you’re moving it to another hardware platform with different parameters for gate fidelity. You’re going to have to redefine all of your optimization problems because the choices you made were optimized for the prior specific hardware.” The Classiq platform handles this type of algorithm adaptability virtually automatically.
When Classiq clients are able to utilize the QAD platform, they develop internal capabilities which will enable them to be nimbler as they approach new QC challenges.
As Yuval Boger added, “We’ll teach you how to express a problem in quantum terms, we’ll teach you how to implement that problem on our platform and we’ll teach you how to run it on any gate-based computer of your choice. Not only will you end up with a running proof of concept, but you’ll then have the expertise to continue the journey on your own. We can take off the training wheels.” So, whereas external software providers or consultants will help provide quantum fish for their customers to eat, Classiq is teaching its customers how to catch their own fish.
Competition
While there are other companies attempting to improve quantum algorithms and/or reduce circuit noise (i.e., Q-CTRL and Quantum Motion), I am aware of only one company focused on higher level quantum algorithm modeling, namely Horizon Quantum Computing based in Singapore. Their tools are designed to expedite and simplify the process of developing quantum software applications and increase the productivity of experienced computing researchers, enabling software developers to harness the full power of quantum processors without prior quantum experience. While this seems like a similar model to Classiq on paper, the Company assures me that they have not run into Horizon in practice and have not faced them regarding any of Classiq’s existing or prospective customers. However, last week it was reported that Horizon raised $12m from Tencent Holdings (although this round has not been confirmed by the company or by PitchBook), so they may become a more formidable competitive force.
Funding
Classiq has completed three investment rounds totaling $51m so are well capitalized. The Company was in the second cohort of Intel’s Ignite accelerator/incubator program and upon completion received $4m in seed funding from Entrée Capital, a London based venture firm. Last year they completed a $10.5m A-Round, led by Team8, a Tel Aviv based venture firm and joined by IN Venture (Sumitomo), OurCrowd, Wing Venture Capital and a follow-on investment from Entrée Capital. Earlier this year they initiated a B-Round raising an additional $33m from IN Venture, Alumni Ventures, Hewlett Packard Pathfinder, and Spike Ventures. Samsung NEXT Ventures, Wing Venture Capital, Entrée Capital, Team8, OurCrowd, Lip-Bu Tan, Harvey Jones, and Phoenix Insurance Company also participated in the round, which is still open. Additional investors including financial giant HSBC and others are also joining the round. This appears to be a strong, diverse, and supportive group of investors including individuals/angels, financial investors and corporate/customer investors, most of which bring connections and business opportunities in addition to their capital.
Collaborations and Partnerships
In the prior post on Collaboration Dominating Quantum Computing, some of Classiq’s various partnerships and collaborations were highlighted, so readers of that post may recognize some of the following information, although this post contains added details:
Classiq/ColdQuanta
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.
Classiq/NVIDIA
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. While some worry that quantum simulation platforms, like those powered by NVIDIA won’t scale beyond 40 or 50 qubits, that is a bit of a misconception in that the threshold noted would only apply if there is full entanglement among all the qubits. Additionally, there will still be important debugging protocols that the Classiq/NVIDIA solution will enhance even once the QCs scale beyond the ability to simulate them on classical devices.
Classiq/NTT
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.
Classiq/Fraunhofer Institute
Classiq has joined the Sequoia project which will focus on software engineering of industrial, hybrid quantum applications and algorithms. The project is coordinated by the Fraunhofer Institute for Industrial Engineering IAO with additional partners including Fraunhofer Institute for Applied Solid State Physics IAF, Fraunhofer Institute for Manufacturing Engineering and Automation IPA, University of Tübingen, Chair of Embedded Systems, FZI Research Center for Information Technology, University of Stuttgart, High Performance Computing Center Stuttgart, and the University of Stuttgart, Institute of Architecture of Application Systems (IAAS). The project is funded by the Ministry of Economic Affairs, Labour and Tourism Baden-Württemberg and will last from 2021 to 2023. Classiq will be working on two main problems within this project including solving mixed-integer linear programming (MILP) problems with state-of-the art (gate-based) quantum computers and solving coupled partial differential equations with the Harrow Hassidim Lloyd (HHL) quantum algorithm. This is an excellent opportunity for Classiq to increase its inclusion in, and exposure to, various academic institutions.
Others
Classiq is participating in the QC access programs of IBM, Amazon and Microsoft and has other collaborative arrangements with Hewlett Packard Enterprise, Sumitomo Corporation, Israel Aerospace Industries, Ltd, The Hebrew University of Jerusalem and Keio University of Tokyo. Some of these arrangements add commercial value, some add academic exposure, and some provide broader industry access.
As can be seen, Classiq has been very active in its collaboration pursuits, and given their agnostic and broad approach to improving quantum algorithms, this is a wise move enmeshing them throughout the QC industry as it continues to grow and evolve.
Learning More
For quantum enthusiasts and investors seeking to learn more about Classiq and their platform I encourage you to visit their website and sign-up for updates. They offer frequent webinars (including one next week on optimization, which you can sign-up for here) and informal demonstrations and other ways to learn more about their products. They are also very active in the various quantum conferences held throughout the year, so you can learn more by speaking with them at any of those in-person and/or on-line events. 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.
For prospective customers, the best way to learn more would be to contact the company and inquire about a paid proof-of-concept around a chosen problem. That will enable you to see how easy it is to model the problem on your chosen platform, what sort of customer support Classiq provides and how flexible the solutions are.
Summary
Classiq has a solid team, strong balance sheet, and highly regarded product. It has entered into a meaningful number of collaborations and partnerships with a wide variety of players. And its business model provides value-add to customers in an agnostic way vis-a-vis quantum hardware and software. While there may some increasing competition, they appear to have a solid first-mover advantage. Some counterweight to those strengths includes being perhaps a bit early to the market (given that most QCs today are modest, and the performance improvements that Classiq can offer, may not yet move the needle) and it is likely that many prospective customers are still getting up the curve on basic QC access. However, these counterweights should be quite short-lived as QCs scale into the 100’s of qubits this year. Smart QC users will engage Classiq now, to ensure they are always optimizing the capabilities of whatever hardware is available at the time. If Classiq can continue its current momentum and become the go-to provider for customers seeking to optimize their quantum algorithms, their customer base should expand quite rapidly and consistently. The following table highlights some of the key attributes of Classiq:
Rating
Apropos of the probabilistic nature of quantum algorithms, I wanted to leverage the nomenclature to create a company rating system and assign a scale to my overall assessment of a company’s potential. Accordingly, I am going to use the formula below when reviewing companies, whereby the “alpha” coefficient correlates with “positivity” (and the formula adheres to the Born rule). Given my overall assessment of Classiq including its strong position as a value-added resource to others working to advance Quantum Computing, the strengths, and capabilities of their Quantum Algorithm Design platform, and their go-to-market strategies, I am assigning the highest rating to Classiq at this time, with an Alpha of 0.95 which equates to an “Exceptional performance expected”.
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.
References:
Boger, Yuval. Interviewed by Russ Fein, April 14, 2022.
If you enjoyed this post, please visit my website and enter your email to receive future posts and updates: http://quantumtech.blog
Russ Fein is a private equity/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 russ@quantumtech.blog.
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.
ColdQuanta
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
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.
ColdQuanta/Strangeworks
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.
Classiq/ColdQuanta
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.
Classiq/NVIDIA
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.
Classiq/NTT
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.
Summary
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.
References:
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.
If you enjoyed this post, please visit my website and enter your email to receive future posts and updates: http://quantumtech.blog
Russ 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 russ@quantumtech.blog.
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
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.
Students
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.”
Conclusion
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.
References:
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.
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
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.blog
Russ 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 russ@quantumtech.blog.
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.
Leadership
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:
Co-founder and senior advisor at Berkshire Partners, Advisory Board Member at New Era Capital. Former partner at Thomas H. Lee Co.
Breyer Capital
Led 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 Siegel
CEO 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 Schmidt
Former Chairman and CEO of Google, additional details noted in prior section.
First Light Capital
A 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 Venture
A NYC/Boston venture firm with interests in software, SaaS, AI and Machine Learning.
Section 32
A Los Altos, California venture firm that invests in healthcare, information technology, life sciences, SaaS, cybersecurity, AI, ML, and other technology-based sectors.
Thomas Tull
Former 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 Ventures
Led by Marc Benioff, Co-Founder, Co-CEO and Chairman at Salesforce.com. Member of the World Economic Forum Board of Trustees.
T. Rowe Price
An 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:
Name
Profile
Marc Porat
Serial entrepreneur of six ventures including General Magic (voice infrastructure software) and PlanetX
Wendy Tan White
CEO, Intrinsic (robotics software and AI), former VP at X, the moonshot factory
Dr. Taher Elgamal
CTO, Security at Salesforce.com; Co-founder of Nok Nok Labs and InfoSec Global; renowned cryptography hailed as the “father of SSL”
Dr. Yossi Matias
VP, Engineering & Research at Google
Dr. Andrew Conrad
CEO, Verily (Google X’s Life Sciences org) and Co-founder of the National Genetics Institute
Dr. Fei-Fei Li
Sequoia Professor of Computer Science at Stanford University; former VP/Chief Scientist of AI/ML at Google Cloud
Dr. Ashton Carter
Former US Secretary of Defense; Director of the Belfer Center for Science & Int’l Affairs at Harvard University’s Kennedy School
Susan M. Gordon
Former Principal Deputy Director of National Intelligence
Admiral Mike Rogers
Retired 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 Richardson
Retired Admiral and former Chief of Naval Operations, US Navy
Blythe Masters
Founding Partner at Motive Partners
Frank Vettese
Co-founder, SummitNorth capital and advisory firm; former CEO of Deloitte
David Siegel
Computer scientist, entrepreneur, co-founder and co-CEO, Two Sigma, a quantitative trading firm managing $60 billion; MIT Board Member
John Seely Brown
Former 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.
Customers
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.
Summary
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.
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.blog
Russ 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 russ@quantumtech.blog.
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):
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,orenable 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.
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 $10trillion 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.
References:
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.blog
Russ 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 russ@quantumtech.blog.
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 russ@quantumtech.blog.
Essentials of Quantum Computing
For an EASY introduction, consider the following:
Article: How to get started in quantum computing, Nature, by David Matthews, March 1, 2021: This was largely my starting point for jumping into the QC world, which provides a good summation as well as links to a few more learning resources.
Online Tutorial: LinkedIn Learning “Introduction to Quantum Computing” is a relatively brief set of videos and interactive quizzes covering a broad range of introductory concepts in a mostly non-technical way.
Book: Quantum Computing: How it works, and why it could change the world, by Amit Katwala, published in 2021 by Wired magazine: No graphs, equations, physics or algebra, just a common language overview.
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.
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 (http://github.com/jackhidary/quantumcomputingbook).
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:
Article: QC companies of 2021: Guide based on 4 ecosystem maps, by Cem Dilmegani, published in AI Multiple, December 17, 2019 and updated February 4, 2022: A good summary of extracts from BCG, Tracxn, Everest Group and Tractics. There are also a number of hyperlinks to additional details.
Article: Commercial applications of quantum computing, by Francesco Bova, Avi Goldfarb and Roger Melko, EPJ Quantum Technology, 2021: Another good summary article also with an extensive list of hyperlinked endnotes.
Article: Quantum Software Development Kits in 2022, by Cem Dilmegani, published in AI Multiple, April 11, 2021 and updated February 8, 2022: A short but dense listing of the key players in QC software development.
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:
Podcasts
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 23rdinterview 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).
Aggregators
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.
Qmunity.tech: 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.blog
Russ 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 russ@quantumtech.blog.
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:
Placing all the physical qubits in an original state, including requisite cryogenics, vacuums, microwave pulses and/or laser pulses, etc.
Manipulating the various qubits to establish Superposition
Applying gates to the qubits to program algorithms, including entangling certain qubits
Applying error correction overhead to confirm the algorithms are performing the desired calculations before decoherence
Applying compiler-level logic as well as various other layers in the QC stack
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)
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:
Scale, or the total number qubits
Quality, or the ability of the qubits to implement circuits before errors enter the system
Speed, or the number of circuits that can be implemented at a given time
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 Super.tech 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.
If you enjoyed this post, please visit my website and enter your email to receive future posts and updates: http://quantumtech.blog
Russ 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 russ@quantumtech.blog.
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:
Prepare a quantum dataset
Evaluate a quantum neural network model
Sample or average measurements
Evaluate a classical neural networks model
Evaluate cost functions
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.
PennyLane
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.
Summary
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.
If you enjoyed this post, please visit my website and enter your email to receive future posts and updates: http://quantumtech.blog
Russ 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 russ@quantumtech.blog.
The Quantum Leap 