The Quantum Space Race

Some have described the rapidly accelerating global push in Quantum Computing as a figurative “space race” given the potential reach of its computational power and its applications in drug development, logistics, material science, and its potential ability to overpower existing encryption techniques. However, this post is focused on the literal quantum space race – the increasing number of quantum devices in orbit and their profound applications. While the fragility of quantum states has been a core challenge in advancing Quantum Computers, that same challenge is a powerful asset for creating ultra-sensitive measuring instruments, and these quantum sensors are now making their way into orbit.

Quantum sensing and quantum communications are making important advances in space in the following areas:

  1. Earth Sensing and Observation
  2. Quantum Key Distribution (QKD) and Secure Communication Networks
  3. Time and Frequency Transfer
  4. Fundamental Physics and Space Exploration

Today there are 77 countries with space agencies,16 of these countries have launch capabilities, and more than 4,500 satellites are currently in earth orbit.  Satellites containing quantum devices are increasingly being placed into orbit, and quantum devices have been used in, and deployed from, the International Space Station.  As Arthur Herman noted in a recent Forbes article: “Quantum communication satellites will become hubs of not only a future quantum internet, but hubs for hack-proof networks for transfer of classified data and communications – not to mention a command-and-control architecture that will be an integral part of space domain dominance” [emphasis added].

The following chart is a partial sampling of existing and planned quantum space launches:

Quantum Space Program Chart

Note: Above chart not intended to be all-inclusive, and some programs have contributions from additional countries. 

We are already increasingly dependent on satellites for global communications and GPS service, among other applications, and space-based experiments are advancing basic science and human knowledge.  Adding the powerful capabilities of quantum technologies will accelerate and expand upon these space-based advances.  The following summarizes some important space-based quantum initiatives:

  1. Earth Sensing and Observation

A key attribute of quantum mechanics which is one of the main rate delimiters in advancing Quantum Computing, is the fragility of the tiny particles placed into a quantum state.  Specifically, attempting to control individual atoms, electrons or photons has been very difficult due to the sensitivities of such particles to external forces including gravity, electromagnetic radiation, temperature fluctuations, and vibrations.  However, it is this sensitivity to such forces that make “qubits” such powerful sensors enabling them to study and assay the earth in detail never before available.

Space (satellite) based quantum sensors can provide reliable detection, imaging, and mapping of underground earth environments from transit tunnels, sewers and water pipes to ancient ruins, mines, and subterranean habitats. There are important civil engineering benefits that more precise sensing can achieve, particularly around large projects (e.g., nuclear power plants, high-speed rail, etc.) where existing subsurface surveys are extremely expensive, time-consuming, and often not as precise as necessary.  Such space-based sensors can also be used to track minute gravitational changes and tectonic shifts that can forewarn of avalanches, earthquakes, volcanic eruptions, or tsunamis.  The strength of Earth’s gravitational field varies from place to place, often due to underlying causes of climate change.   Variations in gravity are caused by factors such as relative positions of mountains and ocean trenches and variations in the density of the Earth’s interior, but also by small fluctuations in underground water reservoirs or changes in ice mass, so gravimetry is an important new tool to help monitor global warming.

  • Quantum Key Distribution (QKD) and Secure Communication Networks

QKD is a secure communication method that uses quantum properties of photons to encrypt secret keys that can be share by two parties to encode their communications.  The technique is considered un-hackable since any attempt to eavesdrop destroys the keys. Current forms of encryption, such as the widely used public-key cryptosystem developed by RSA, rely on the difficulty of solving mathematical problems whereas QKD instead relies on physical processes. In quantum physics, there is a “no-cloning” theorem which states that it is impossible to create identical copies of an unknown quantum state.  This prevents hackers from simply copying the quantum encoded information.  Another quantum property known as “observer effect” causes quantum states to change upon observation and therefore, if anyone were to try and read the QKD it would change it and that change would be instantly known by the parties involved.  (If interested in learning more about QKD please see here.)

QKD has already been successfully implemented via fiber optic cables, but only over short distances.  Beyond 100 kilometers (about 60 miles) the signal degrades and beyond 300 kilometers the information transmission becomes prohibitively slow (i.e., only about one bit per second).  In fact, the signal degradation increases exponentially as the distance increases.  By using satellites in low-earth orbit (LEO) to send and receive transmissions via line-of-sight, this distance challenge can be largely overcome.  LEO orbits can provide line-of-sight transmission between earth-based ground stations that are up to about 700 kilometers (about 430 miles) apart, although this limitation can be exceeded if the key can be stored in the satellite while it orbits or, preferably, by relaying the signal among connected satellites.

Naturally, un-hackable communications is a key objective of many governments as well as certain industrial firms, hence the broad number of countries currently working on space-based QKD.

  • Time and Frequency Transfer

An overwhelming array of modern conveniences are reliant upon highly accurate clocks.  [In fact, this is such a prevalent and important observation that my next post will be dedicated to need for more precise time measurement].  Many electric power grids use clocks to fine-tune current flow.  Telecom networks rely on GPS clocks to keep cell towers synchronized so calls can be passed between them.  The finance sector uses clocks to timestamp ATM, credit card and high-frequency trades.  Doppler radar, seismic monitoring and even multi-camera sequencing for film production all use highly precise clocks. Today’s earth-based atomic clocks are extremely accurate, and you can readily synchronize your computer to the atomic clock of your choice.  However, relying on existing atomic clocks for timestamping, such as currently done for GPS satellites, is becoming increasingly challenging.  GPS navigation is currently accurate to about three meters (about 10 feet), so it presents challenges to using it for autonomous driving, as one example. 

In order to improve on existing time keeping and related applications, we need both a more accurate clock as well as more precise dissemination and sharing of time.  Quantum technologies can improve time accuracy by orders of magnitude and placing them in space can enhance dissemination.  Increased time accuracy will improve current communications and geolocation services as well as enable new applications and a space-based quantum clock can enable long-range transfer timing.

  • Fundamental Physics and Space Exploration

NASA’s Cold Atom Lab aboard the International Space Station (ISS) has used atom interferometry to create a new generation of exquisitely precise quantum sensors that scientists are using to explore the universe. Applications of these spaceborne quantum sensors include tests of general relativity, searches for dark energy and gravitational waves, spacecraft navigation and drag referencing, and gravity science, including planetary geodesy—the study of a planet’s shape, orientation, and gravity field.

In 2019, the image of a supermassive black hole was created using earth-based synthetic aperture telescopes.  By precisely measuring the arrival time of radio waves at two different locations, an image of their source was created.  Because visible light wavelengths are much shorter than radio waves (nanometers vs meters), more sensitive detectors and clocks are required to use this methodology for visible light, such as those now being placed in orbit.  The resolution of such an image would match the resolution of a conventional telescope with an aperture equal to the distance between the two satellites.   Such telescopes would be extremely sensitive, potentially enabling astronomers to study planets around other stars in vast detail. 

Space-based quantum sensors will also be crucial for space exploration.  As spacecraft venture further away from Earth, the ability to provide navigational instructions diminishes.  Naturally “GPS” would be unavailable in deep space, and Earth-based control signals have increased time lag times as spacecrafts travel further away.  Additionally, if such Earth-based navigational commands are not precise enough, the target craft may miss its destination completely.  Sensors that can measure a vehicle’s acceleration and rotation can enable navigation without requiring external commands.  In addition, space-based quantum sensors are planned to help search for water and other resources on the moon and Mars.

Conclusions

The pace of advances in quantum science is rapid and paradigm shifting.  While Quantum Computing gets most of the headlines, important advances in quantum sensing and communication is also advancing rapidly including via deployment in space.  By placing powerful quantum devices into space, significant advances in earth observation, space exploration and secure communications are being achieved.  Given the intense competitive nature of terrestrial quantum advances, extending this to a “space race” is inevitable and, in fact, is already underway.  Readers should anticipate more and more headlines on this topic, and I look forward to providing periodic updates.

Disclosure: The author 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.


References:

Herman, Arthur, “The Quantum Space Race is Here,” Forbes.com, October 20, 2022.

Quantum Technologies Take Flight, from Science.nasa.gov, accessed November 8, 2022.

Taking Climate Monitoring into the Future with Quantum, from http://www.esa.int, accessed October 30, 2022.

Kaltenbaek, Acin, Bascardi, Bianco, Bouyer et al, “Quantum Technologies in Space,” arXiv:2017.01387v1 [quant-ph] July 3, 2021.

What are Quantum Sensors, from utmel.com, accessed October 30, 2022.

van Velzen, Neelakantaiah, Owen and Bauner,  “Sustainable Development – How Quantum Technologies Can Help Drive the UN’s Sustainability Development Goals,” Capgemeni White Paper, accessed October 30, 2022.

The Future of Space-Based Quantum Technology, Discover Magazine, July 22, 2021.

NASA-Industry Team Creates and Demonstrates First Quantum Sensor for Satellite Gravimetry,” from http://www.nasa.gov, accessed October 30, 2022.

Kerstel, Gardelein, Barthelemy, Fink, Joshi, Ursin, “Nanobob: a CubeSat  mission concept for quantum communication experiments in an uplink configuration”, EPJ Quantum Technology, June 22, 2018.

SES Selects Arianespace to Launch EAGLE-1 Satellite for Europe’s Quantum Cryptography,” Arianespace Press Release, November 9, 2022.

Leprince-Ringuet, Daphne, “Quantum sensors could soon be heading into space,” ZDNet.com, March 3, 2021.

Pultarova, Tereza, “UK company to start sending secret quantum keys with satellites in 2023,” http://www.Space.com, June 23, 2021.

“Quantum Encryption and Science Satellite (QEYSSat), from www.asc-gc.ca, accessed October 30, 2022.

Werner, Debra, “Australian startup develops quantum sensors for space applications,” Spacenews.com, March 3, 2021.

Glass, Dan, “What Happens if GPS Fails?”, The Atlantic, June 13, 2016.

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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.

        Quantum Computing Modalities – A Qubit Primer Revisited

In December 2021, in an early iteration of this Blog, I described the various qubit modalities in use by some of the Quantum Computing (QC) hardware players.  A lot has happened since that post, so I thought it would be constructive to revisit the topic.

When that earlier post was published (click here if interested in reviewing), it described 10 leading quantum hardware companies focusing on four core qubit types (superconducting, trapped ions, photonics and quantum dots).  Today there are dozens of quantum hardware companies, a few additional common modalities (notably neutral atoms) and significant advances made across the spectrum.

Qubit Dynamics

While many articles describing and comparing QCs focus on the number of qubits, this core number belies the complexity in comparing actual QC performance due to additional limitations described below.  Qubit count is the equivalent of only using horsepower to describe a car.  While horsepower is an important metric, most car buyers are equally if not more focused on comfort, handling, fuel economy, styling, etc.  Some effort has been made to “consolidate” these variables for QC into a single performance metric (such as Quantum Volume, CLOPS (circuit layer operations per second) or QED-C’s Benchmarks), although no single measurement has yet been adopted by the broad QC ecosystem.  For the casual reader, I’d caution you to not focus too much on the number of qubits a given QC has.  While “more is better” is generally a useful mantra, as you’ll see below, it is not that simple.

As you may know or recall, placing qubits in a superposition (both “0” and “1” at the same time) and entangling multiple qubits where one is dependent on the status of the other (entanglement) are two fundamental quantum properties which help empower Quantum Computers and allow them to perform certain calculations that can’t easily be executed on traditional computers.  Before we review the various types of qubits (i.e., quantum hardware platforms), it may be helpful to summarize some of the limitations faced when placing qubits in superposition and/or entangling multiple qubits, and discuss the key metrics used to measure these properties.

Two-qubit Gate Error Rate: Entanglement is a core property of QCs and the two-qubit gate error rate is the second-most-often reported metric (after qubit count).  An error rate of 1% is the equivalent of 99% gate fidelity.  You may have come across the concept of a ‘CNOT gate’ or controlled-not gate, which simply takes two qubits and when the first (control qubit) is in a desired state, it flips the second (target qubit).  While this sounds basic and simplistic, it is this correlating of the qubits that enables the exponential speedup of QCs.  Said another way, it is a method for enabling QCs to analyze multiple pathways simultaneously, and so is truly a fundamental property being leveraged by QCs. Many in the industry suggest that 2Q fidelities exceeding 99.99% will be required to achieve quantum advantage and some modalities are approaching that (for example, IonQ has achieved 99.92%), but most are still considerably below that threshold.

Single qubit/Rotation Error Rate: Single qubit gates, also often referred to as “rotations” adjust the qubits around various axes (i.e., x-axis, y-axis, and z-axis).  In classical computing, you may be familiar with a NOT gate, which essentially returns the opposite of whatever is read by the machine.  So, a NOT applied to a 0 “flips” it to a 1.  Similarly, in quantum computing, we have the X-Gate, which rotates the qubit 180-degrees (around the X-axis) and so also takes a 0 and “flips” it to a 1.  Given the exquisite control required to manipulate qubits, it is possible that the pulse instructing the qubit to “flip” may only apply 179-degress of rotation instead of the required 180 and therefore lead to some error, especially if such imprecision impacts many qubits within an algorithm.

Decoherence Time (T1 and T2): T1 (qubit lifetime) and T2 (qubit coherence time) are effectively two ways to view equivalent information, namely “how long do the qubits remain in a state useful for computation?”    Specifically, T1 measures a qubit lifetime, or for how long we can distinguish a 1 from a 0, while T2 is focused on phase coherence, a more subtle but also crucial aspect of qubit performance. Many early QC modalities such as superconducting have modest T2 lifetimes, capping out at 100 microseconds (or millionths of a second) whereas some recent entrants such as neutral atoms, have achieved T2 as long as 10 seconds and certain trapped ions have extended that to 50 seconds.  These many orders of magnitude difference in T2 among qubit modalities is a key differentiator among them.

Gate Speed: Is a metric that measures how quickly a QC can perform a given quantum gate.  This is especially important relative to the decoherence time noted above, in that the QC must implement its gates BEFORE the system breaks down or decoheres. Gate speed will become increasingly important as a raw metric of time-to-solution where microseconds add up.  Interestingly, the modalities with relatively short T2 times (i.e., superconducting, and photonic) generally have the fastest gate speeds (measured in nanoseconds or billionths of a second).

Connectivity: Sometimes referred to as topology, is a general layout of the qubits in a grid and is concerned with how many neighboring qubits a given qubit can interact with. In many standard layouts, the qubits are lined up in rows and columns with each qubit able to connect to its four “nearest neighbors”.    Other systems can have “all-to-all” qubit connectivity, meaning every qubit is connected to every other one.  If two qubits can’t directly interact with each other, “swaps” can be inserted, to move the information around and enable virtual connections, however this leads to added overhead, which translates into increased error rates. 

SPAM (State Preparation and Measurement) Error Rate: At the start of any quantum algorithm, the user must first set the initial state, and then in the end, that user must measure the result. SPAM error measures the likelihood of a system doing this correctly.  A 1% SPAM error on a five-qubit system provides a very high likelihood that the results will be read correctly (99%5=95%) but as the system scales, this becomes more problematic. 

Qubit Modalities

When the bits created for classical computing were first created, there were several different transistor designs developed. Similarly, today there are many ways to create a qubit and there are crucial performance trade-offs among them. The following is a brief overview of some of the more common types:

Superconducting Qubits: Some leading Quantum Computing firms including Google and IBM are using superconducting transmons as qubits, the core of which is a Josephson Junction which consists of a pair of superconducting metal strips separated by a tiny gap of just one nanometer (which is less than the width of a DNA molecule). The superconducting state, achieved at near absolute-zero temperatures, allows a resistance-free oscillation back and forth around a circuit loop. A microwave resonator then excites the current into a superposition state and the quantum effects are a result of how the electrons then cross this gap. Superconducting qubits have been used for many years so there is abundant experimental knowledge, and they appear to be quite scalable. However, the requirement to operate near absolute zero temperature adds a layer of complexity and makes some of the measurement instrumentation difficult to engineer due to the low temperature environment.

Trapped Ions: Another common qubit construct utilizes the differential in charge that certain elemental ions exhibit. Ions are normal atoms that have gained or lost electrons, thus acquiring an electrical charge. Such charged atoms can be held in place via electric fields and the energy states of the outer electrons can be manipulated using lasers to excite or cool the target electron. These target electrons move or “leap” (the origin of the term “quantum leap”) between outer orbits, as they absorb or emit single photons. These photons are measured using photo-multiplier tubes (PMT’s) or charge-coupled device (CCD) cameras. Trapped Ions are highly accurate and stable although are slow to react and need the coordinated control of many lasers.

Photonic Qubits: Photons do not have mass or charge and therefore do not interact with each other, making them ideal candidates of quantum information processing. However, this same feature makes two-gate implementation particularly challenging.  Photons are manipulated using phase shifters and beam splitters and are sent through a maze of optical channels on a specially designed chip where they are measured by their horizontal or vertical polarity.

Neutral Atoms: Sometimes referred to as “cold atoms”

are built from an array of individual atoms that are trapped in a room-temperature vacuum and chilled to ultra-low temperatures by using lasers as optical “tweezers” to restrict the movement of the individual atoms and thereby chill them.   These neutral atoms can be put into a highly excited state by firing laser pulses at them which expands the radius of the outer electron (a Rydberg state), which can be used to entangle them with each other. In addition to large connectivity, neutral atoms can implement multi-qubit gates involving more than 2 qubits, which is instrumental in several quantum algorithms (i.e., Grover search) and highly efficient for Toffoli (CCNOT) gates.

Semiconductor/Silicon Dots: A quantum dot is a nanoparticle created from any semiconductor material such as cadmium sulfide, germanium, or similar elements, but most often from silicon (due to the large amount of knowledge derived from decades of silicon chip manufacturing in the semiconductor industry). Artificial atoms are created by adding an electron to a pure silicon atom which is held in place using electrical fields. The spin of the electron is then controlled and measured via microwaves.

The following table highlights some of the features of these various qubit modalities, as of Oct. 2022:

There are a few other modalities including NV Diamonds, Topological, Nuclear Magnetic Resonance (which seems more experimental and very difficult to scale) and Quantum Annealing (used by D-Wave, one of the first firms to offer commercial “Quantum” computers, but annealing is not a true gate-capable construct) and it is likely that more methodologies will be developed.

The following table summarizes some of the benefits and challenges along with select current proponents of key qubit technologies currently in use:

The table above is not intended to be all-inclusive.  In fact, there is an excellent compendium of qubit technologies put out by Doug Finke’s Quantum Computing Report which can be accessed here (behind a pay wall, but well worth the fee), and which includes over 150 different quantum hardware computing programs/efforts. A special thank-you also to David Shaw and his Fact Based Insight website which has covered this topic in great detail.

Conclusions

As noted in this post, there have been significant advancements in Quantum Computing hardware over the past year or so and I expect this momentum will continue in 2023.  Presently there are QCs with 10s to 100s of qubits, and the coherence, connect-ability and control on these early machines continues to improve.  In 2023 we should see machines with 1000’s of qubits (e.g., IBM is on pace to release their Osprey QC with 433 qubits before year end and their 1,121 qubit Condor QC next year).  Adding sophisticated control and advanced algorithm compilation further extends the capability of these early machines.  Whether and when we can achieve universally recognized quantum advantage (i.e., these QCs performing operations that existing supercomputers cannot do) during this NISQ (noisy intermediate stage quantum) era remains to be seen, but this author believes this will happen in the ’23-’24 timeframe and is excited to continue tracking (and reporting on) the progress.

Disclosure: The author has modest positions in some stocks discussed in this review 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.


References:

Qubit images from Science, C. Bickel, December 2016, Science, V. Altounian, September 2018, New Journal of Physics, Lianghui, Yong, Zhengo-Wei, Guang-Can and Xingxiang, June 2010

Performance Tables and additional modality details from Fact Based Insight, Accessed October 2022

Comparing Quantum Computers: Metrics and Monroney,” IonQ, February 18, 2022

Shaw, David, ‘Quantum Hardware Outlook – 2022,” Fact Based Insight, December 13, 2021

Bobier, Langione, Tao and Gourevitch, “What Happens When ‘If’ Turns to ‘When’ in Quantum Computing?”, BCG, July 2021

Shaw, David, “Quantum Hardware Into the Quantum Jungle”, Fact Based Insight, July 21, 2020

Silverio, H., Grijalva, S, et. al., “Pulser: An open-source package for the design of pulse sequences in programmable neutral-atom arrays,” arXiv:2104.15044v3 [quant-ph], January 12, 2022.

Henriet, L., Beguin, L., et. al., “Quantum Computing with Neutral Atoms,” arXiv:2006.1232v2 [quant-ph], September 18, 2020

Ray, Amit, “7 Primary Qubit Technologies for Quantum Computing”, December 10, 2018

Lichfield, Gideon, “Inside the race to build the best quantum computer on Earth”, MIT Technology Review, February 26, 2020

Quantum Computing “Noise”

We all know what “noise” is. And we all appreciate that it is usually an unwelcomed invasion of our peace and quiet.  Screaming babies on airplanes, jackhammers in the street, leaf blowers outside your window – all can ruin an otherwise tranquil setting.  “Noise” in computer lingo represents a similar disconcerting situation.  In Quantum Computing (QC), you likely have come across the concept of noise as a major obstacle to QC’s achieving their potential.  In fact, John Preskill, a professor of theoretical physics at Caltech and one of the pioneers of QC, coined the acronym “NISQ”, standing for Noisy Intermediate-Scale Quantum Computers which is used to describe today’s QC stage.  There are several significant challenges facing QC makers today, and “noise” is one of the most difficult to overcome. 

Quantum Computing Noise

There are many causes for the underlying noise in QCs.  In order to best visualize and understand this, here is a reminder of how qubits (the underlying components of QC processing, comprised of individual atoms, photons or electrons) store and manipulate information:

The graphic above depicts a few rotations of a qubit, with the blue arrows pointing to various points before and after a rotation (various rotations are implemented via gates representing algorithm commands) and the red arrow showing the axis of rotation.  The ending position of the blue arrow contains important and precise information but can move incorrectly due to several noise factors. Here are a few of the core sources:

  • The Environment: Qubits are exquisitely sensitive to any changes in their environment.  Small changes in temperature or stray electrical or magnetics fields can disturb qubits and cause a degradation of the information.  Even weak galactic space radiation can push qubits and thereby degrade them.
  • Crosstalk: Quantum Computers are powered by qubits acting together.  Generally, individual qubits are manipulated by lasers or microwaves.  However, sometimes the laser or microwave signal can impact nearby qubits as well as the target qubit, an issue knows as crosstalk.
  • Quantum Decoherence: A qubit’s quantum state deteriorates rapidly, often even after just fractions of a second, requiring QCs to initiate and complete their algorithms before quantum states collapse.
  • Implementation Errors: The commands or gates of a quantum algorithm apply various rotations to the qubit, which are implemented by laser or microwave pulses which can also be somewhat imprecise.  For example an X-Gate, which is analogous to a NOT gate in a classical computer, essentially “flips” the qubit rotating it by 180 degrees.  If the pulse command to do this only leads to a 179-degree rotation, the subsequent calculations will be off by a potentially meaningful amount.

You may be familiar with the term “five 9’s” which has often been used in the context of super-high performance.  It generally means a system with 99.999% accuracy, or only one error per 100,000 instances. For service level agreements with, say your cloud provider, five nines would mean less than 5.26 minutes of downtime per year.  It’s a high standard, recognizing the reality that certain systems suffer from various unknown or unpredictable challenges.  While Quantum Computer makers continue to improve upon the fidelities of their qubits (the underlying physical components which process quantum gates and algorithms), none have been able to achieve greater than 99.9% two-gate fidelities.  While that may sound high and would likely have been an acceptable grade on your physics final, it is not enough to enable Quantum Computers to perform the complex algorithms necessary for QCs to outperform existing classical computers. 

The non-technical takeaway: Quantum Computations are run via qubits which are very difficult to control, are vulnerable to the tiniest environmental changes and have a natural tendency to move, leading to a degradation of the information.

Error correction is the single largest challenge facing QC advancement today, and there are many ways that companies are addressing this issue.

How to Overcome Noise Constraints in Quantum Computing

In the 19th century, ships typically carried clocks using the time in Greenwich in combination with the sun’s position in the sky for determining longitude during long trips.  However, an incorrect clock could lead to dangerous navigational errors, so ships often carried three clocks.  Two clocks showing differing times would detect a fault in one, but three were needed to identify which one was faulty (if two matched the third one was off).  This is an example of a repetition code, where information is encoded redundantly in multiple devices, enabling detection and correction of a fault.   In QCs, because measurement fundamentally disturbs quantum information, we can’t do interim measurements to identify errors because that would terminate the process, so data is shared among multiple qubits, often referred to as ‘ancillary’ qubits, ‘syndrome’ qubits or ‘helper’ qubits. A series of gates entangles these helper qubits with the original qubits, which effectively transfers noise from the system to multiple helpers.  We can then measure the helpers via parity check, which, like those redundant clocks, can reveal errors without touching or measuring the original system.  However, the trade-off is the requirement for many physical qubits to act as helpers, adding enormous overhead to QCs.

Also, since each step of a quantum algorithm is an opportunity for noise to be introduced, efforts to quicken the runtime or reduce the number of steps (i.e., gates) are intended to minimize the opportunity for noise to corrupt the output.  In addition to repetition code methods of finding and correcting errors and overall efforts to minimize circuit depth, there are a few other tools being used to tackle quantum noise. A high-level view of the quantum computing software “stack” should help provide some context for these added methods:

The graphic above is generally referred to as the “full stack” and there are opportunities at each level of the stack to help compensate for or minimize noise.  Here are a few methods being deployed:

Quantum Control:  At the qubit level, often referred to as the “metal”, engineers continue to optimize the pulses and control signals focused on the qubits as well as create modalities with increasing coherence times.  Various ways that the qubits are aligned and/or inter-connected affect this level and advances are being continually announced.

Hardware Aware Optimization:  At the Machine Instruction level, focus on transpiler efficiencies can reduce errors and minimize noise impacts.  Again, various qubit configurations as well as the specific modalities utilized (superconducting vs optical vs ion vs cold atom, etc.) have an impact on the performance of the algorithms and attention to this level of the stack provides another opportunity for noise reduction.

Compiler Efficiency: Circuit optimization is a target of many players in the QC space.  Tools that re-write algorithms to focus on this level of the stack is a growing and important part of the ecosystem.  For example, efficient usage of ancillary qubits and/or resetting them quickly to be re-utilized requires less run-time and less steps, which means less opportunity for noise to impact the programs.

Algorithm efficiency: There are many ways to write quantum algorithms so ensuring that the code is as efficient as possible (i.e., eliminating redundant steps or minimizing needs to reset or recalibrate qubits) is another opportunity to minimize noise.  The more efficient the code, the quicker it can run, or the shorter its circuit depth needs to be.

Many Shots: A final tool which is a standard procedure in quantum algorithms, is to run the algorithm many times. Each run is referred to as a “shot” and typical algorithms are run with 1000’s of shots.  By averaging the output of these shots, a “regression to the mean” is often realized, meaning the averaging of the results helps various noise impacts cancel each other out.  [The fact that quantum algorithms are probabilistic and not deterministic is a major reason for the redundant shots, but this redundancy is also a tool to help overcome noise].

The non-technical takeaway: Noise is a major problem impacting the ability of Quantum Computers to achieve their potential.  Until fault-tolerant hardware can be developed, quantum engineers are deploying several creative ways to overcome noise in current QCs.

Quantum Companies Addressing Quantum Noise

There are a number of players focused on noise reduction and deploying inventive solutions to optimize the performance of todays quantum machines.  Some of these methodologies can achieve performance improvements of orders of magnitude, so these methodologies are yielding significant improvements.  As the quantum hardware players release ever-larger quantum machines (for example, IBM has announced it will release a machine with more than 1,000 qubits next year) these error correcting strategies will greatly accelerate the ability of QCs to achieve quantum advantage, with many prognosticators (including yours truly) expecting such achievement sometime next year (at least for certain types of problems).  The following is a brief overview of some of the players that offer various quantum noise-reduction solutions:

Classiq: Their flexible and powerful platform automatically creates optimized and hardware-aware circuits form high-level functional models.  It automates and simplifies the difficult process of creating quantum algorithms. 

Parity QC: Develops blueprints for quantum computers based on their ParityQC architecture creating quantum computers which are scalable by radically reducing the control complexity. This allows them to provide a fully programmable, parallelizable (no SWAP gates), and scalable architecture which can be built with a greatly reduced complexity and a quantum optimization architecture which is independent from the problem. Due to its ability to parallelize gates, the ParityQC Architecture introduces algorithms based on global gates. In each step, a pattern of gates are executed at the same time. This removes the need to implement a control signal for each individual gate and only requires ONE single control signal for all gates instead. This provides a huge advantage for the hardware design and a route to mitigate cross-talk errors during qubit design.

Q-CTRL: Their quantum control infrastructure software for R&D professionals and quantum computing end users delivers the highest performance error-correcting and suppressing techniques globally, and provides a unique capability accelerating the pathway to the first useful quantum computers. This foundational technology also applies to a new generation of quantum sensors, and enables Q-CTRL to shape and underpin every application of quantum technology.

Riverlane: A quantum software provider with a whole stack focus, aiming to squeeze out every bit of efficiency.  Their Deltaflow.OS®  operating system is compatible with all current quantum hardware platforms including both gate-based and annealing methods. This allows them to provide a fully programmable, parallelizable (no SWAP gates), and scalable architecture which can be built with a greatly reduced complexity and a quantum optimization architecture in conjunction with hardware partners to optimize the design of their architecture for error correction.

Super.tech: This successful member of the first cohort of the Chicago Quantum Exchange/ UChicago Duality Accelerator was acquired by ColdQuanta earlier this year. Their SuperstaQ quantum software platform is optimized across the entire quantum stack enabling 2x reductions in error on typical quantum programs. SuperstaQ includes a library of sophisticated error mitigation techniques, including dynamical decoupling, excited state promotion, and zero noise extrapolation. SuperstaQ automatically optimizes quantum programs based on the target hardware’s pulse-level native gates.

Xanadu: Their PennyLane software is leading programming tool leveraging a cross-platform Python library which enables quantum differentiable programming — that enables seamless integration with machine learning tools. PennyLane also supports a comprehensive set of features, simulators, hardware, and community-led resources that enable users of all levels to easily build, optimize and deploy quantum-classical applications.

Zapata: a quantum computing software company that develops solutions for a wide range of industries. Zapata’s Orquestra™ platform allows users to compose quantum-enabled workflows and orchestrate their execution across classical and quantum technologies. Orquestra combines a powerful software platform, quantum algorithm libraries, and example workflows across machine learning, simulation and optimization. Orquestra automatically scales up and exploits task parallelization opportunities to run quantum algorithms faster.

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:

Ackermann, Thomas J., “Noisy Intermediate Scale Quantum (NISQ) Technology,” BGP4.com, October 17, 2018.

Bertels, Sarac, Sarkar and Ashraf, “Quantum Computing – From NISQ to PISQ”, arXiv:2106.11840v3[quant-ph], April 15, 2022.

Fellner, Messinger, Ender and Lechner, “Universal Parity Quantum Computing,” arXiv:2205.09505v1[quant-ph], May 19, 2022.

Lee, Chris, “No sugarcoating: Donut math yields way to make qubits last longer,” Science, July 2022.

Nazario, Zaira, “How to Fix Quantum Computer Bugs,” Scientific American, May 1, 2022

Russell, John, “New IBM Blog Details Path to Quantum Advantage in 2023,” HPCWire, July 19, 2022.

Shaw, David, “Quantum Software Outlook 2022”, Fact Based Insight, January 17, 2022.

Smith-Goodson, Paul, “Quantinuum Makes a Significant Quantum Computing Breakthrough by Connecting the Dots of its Previous Research,” Forbes, 8/4/22.

Zapata Computing Publishes New Research on using Orquestra Platform to Implement Fundamental Subroutine for Quantum Algorithms,” Zapatacomputing.com, Oct. 21, 2021.

Stace, Thomas M. and Biercuk, Michael J., “Quantum Error Correction: Time to Make it Work,” Spectrum.IEEE.org, June 26, 2022.

Photo by Ketut Subiyanto: https://www.pexels.com/photo/man-in-blue-button-up-shirt-smiling-4584184/

Graphic from Dunning, Alexander & Gregory, Rachel & Bateman, James & Cooper, Nathan & Himsworth, Matthew & Jones, Jonathan & Freegarde, Tim. Composite pulses for interferometry in a thermal cold atom cloud. Physical Review A. 90. 033608. 10.1103/PhysRevA.90.033608. (2014).

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.

Shifting Quantum Investment Dynamics

There have been an increasing number of articles describing a coming “Quantum Winter”.  While I am still extremely bullish on the sector and do not believe the industry will suffer a full abandonment by investors, the blunt reality is that the investment winds are shifting and will require a more sober view on quantum companies in the near-term.

Here are two graphics to help frame this situation.  The first depicts recent movements in the public markets, and the second traces key venture investments.   Specifically, the table below highlights the decline in stock price of four publicly traded quantum computing companies including ATOS (European based broad information tech), IONQ (trapped ion quantum computers), Quantum Computing Inc. (quantum software provider) and Rigetti (superconducting full-stack quantum computers).  Together, these companies are off 75% from their recent highs whereas the broader NASDAQ index is down 30%.

So, while the overall market is suffering a broad decline including the tech-heavy NASDAQ, this bucket of quantum stocks is down more than double the amount.  It is somewhat encouraging that these firms were able to go public recently, but their poor stock performance will make it increasingly difficult for other early-stage quantum companies to follow suit.  These four firms are a small sample of the overall quantum industry and the chart is not market-weighted, so this isn’t a statistically clean analysis, but the undeniable conclusion is that investors in publicly traded quantum stocks are looking at a very steep hill regarding their quantum stock results (as are employees in these companies granted stock options at anything close to the IPO prices) and private quantum companies considering public markets as a way to raise operating capital will likely need to wait at least a few quarters, if not longer, before they could consider an IPO.

As for the private sector, venture funding of quantum companies had a break-out year in 2021 with nearly $1.5 billion invested in the top 20 funded quantum businesses.  And while 2022 had started out strong, we’ve seen a significant decline in funding in the recently ended quarter, as highlighted in the last column below.

Source: PitchBook (excludes grants and debt financing)

A few additional observations:

  • The largest equity rounds were for firms creating quantum hardware.  The bar to entry for others working on various qubit modalities is now exceptionally high.  This is not to say others won’t be added to the list, but the days of seed-funded quantum hardware companies is likely over, rather major institutional support will be required.
  • Venture led boards are beginning to urge an increase from 24 months of operating capital to 36 months, to ensure adequate runway.  This will necessitate a lowering spend by portfolio companies which will translate into longer milestone timelines.
  • Given the overall market malaise and recent pull-back by venture investors generally, new QC rounds will become more challenging, and down-rounds are likely.  Down rounds have lingering and residual negative effects on capital markets, so this undoubtably will cause some heartburn in the industry.
  • Given the existing dearth of talent in the quantum information industry, combined with rationalized firm valuations and needs to preserve capital, I expect we’ll see increasing M&A activity.

It’s well known that markets move in cycles, so difficult fundraising environments are to be expected.  That said, I’m still extremely bullish on the space in general, especially taking a 5-10 year view which is the time range most often cited for achievement of consistent quantum advantage.

My general take-away from this analysis is that valuations for quantum companies will become rationalized in the next few quarters, providing an attractive investment window.  In addition, while quantum hardware companies have taken much of the spotlight, there are many other players in the quantum ecosystem that will benefit from broader industry adoption, particularly those involved with the “picks and shovels” of QC such as cryogenics, lasers, optics, controllers, vacuums, etc., and certainly for the software providers, especially those agnostic to the form of hardware used.  Quantum sensing and communications are also appropriate focus areas.

In summary, I’m not a believer in a full-on quantum winter, but we are in for some near-term challenges and disruption in the Quantum Computing arena.  Tighter budgets, more difficult access to funding, and laser-focus on milestone achievement will be the norm.  Of course, the evaporating liquidity will make milestone achievement that much more difficult, so there is likely to be some negative feedback loop effect as well.  However, in some sense, this will be positive long-term in that “survival of the fittest” will winnow away some of the marginal players.  I predict and expect the industry will come away stronger and I look forward to the eventual “Quantum Spring”.

Disclosure: The author maintains personal long positions in certain companies mentioned herein but does not have any business relationship with any company referenced 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.

The Metaphysics of Quantum Computing

 When I created this blog, my stated purpose was to follow Quantum Computing (QC) from the perspective of an investor.  To date, I have generally posted blogs that either covered technical aspects of QC (e.g., this post explaining superposition and entanglement), or showcased the companies involved in commercializing QC (e.g., this post on the evolving ecosystem).  However, I hope you’ll indulge me a bit for this latest post, which approaches QC from a philosophical perspective.  It’s an aspect of this field that originally gripped my attention and which underlies much of why quantum mechanics conjures such non-intuitive conclusions.  Here are a few concepts that will be covered, each of which likely induces head-scratching:

  • Wave/Particle Duality
  • Matter/Energy Equivalence
  • Superposition and Entanglement
  • The Observer Effect
  • The Uncertainty Principle
  • “Imaginary” Numbers

As many of you may already know, a core feature of quantum mechanics concerns the “duality” between particles and waves. Certain aspects also deal with the interchange of matter and energy (you are already likely familiar with Einstein’s E=MC2 equation which famously and simply showed the equivalence between matter and energy). These somewhat non-intuitive principles underpin some fascinating philosophical questions regarding QC. That said, I am approaching this as a lay person, so will not debate any of the theological roots or delve deeply into the underlying physics. However, I hope you will enjoy this mental exercise and that it will spur your curiosity to dig in deeper yourself.

The Quantum Computing “Chicken-and-Egg” Quandary

If you search for resources about the origin of Quantum Computing, you will invariably come across a quote by Richard Feynman, generally cited as the father of QC. In 1981, Feynman said:

“Nature isn’t classical, dammit, and if you want to make a simulation of nature, you better make it quantum mechanical…”

Most current descriptions about how QCs work approach it from the qubit perspective.  How to harness the quantum mechanical features of the underlying qubit, be it an atom, electron or photon.  A new form of computing paradigm, where we use machines to solve problems or equations that current classical computers would take too long to solve.  While this is truly fascinating, and I am confident it will unlock massive opportunities (and value), it is a bit “backwards” from what Feynman was suggesting.  His premise was focused on “simulating nature” and since nature is governed by quantum physics, he was suggesting we needed to use quantum physics to better understand nature.  It is expected that as QCs become larger and more powerful, we will be able to simulate nature to create better batteries, fertilizers, and medicines, among other things.  But QCs will also enable us to answer questions we’ve never thought to ask and which would essentially be gibberish to classical computing processes. 

The metaphysics of this concept revolves around using QCs to create better QCs.  As we work to scale existing QCs which currently contain tens or hundreds of qubits, an obvious early question is “how do we build better and larger configurations of qubits?” As industry drives towards 1,000,000-qubit machines, it seems obvious (at least to me) that it will take QCs to optimize the configurations of these larger QCs.  What is the upper limit of the capabilities such a self-supporting loop can create?  This 1,000,000-qubit goal assumes “noisy” qubits, so it is thought that we need 1 million qubits to net-out to 100 logical qubits, and much has been written about the awesome power of 100 logical qubits…but why stop there?  What if we had 1,000 or even 1,000,000 logical qubits?  The power of such a machine would, essentially, be so massive as to be indescribable. 

More on Wave-Particle Duality

 Quantum computers derive their power from quantum mechanics, which is the study of the physical properties of nature at the scale of atoms, photons and subatomic particles.  Many of the fundamental properties of quantum mechanics revolve around the behaviors of these particles, which exhibit characteristics of both particles and waves.  Intuitively, we understand particle behavior which guides the path of a baseball or the motion of a billiard ball.  Similarly, we are familiar with waves and how they can sometimes cancel each other or enhance each other.  However, when particles exhibit both properties simultaneously, non-intuitive things happen, such as superposition and entanglement.  While non-intuitive, these features are well proven experimentally and can be explained and predicted using established mathematics so we must wrestle with the fact that something so non-intuitive is occurring at the smallest scales.  Conversely, I have yet to find a satisfactory explanation or formula to describe “the observer effect”.  For those of you not familiar with this feature of quantum mechanics, it essentially says the act of measuring something (i.e., observing it) actually changes it.  An example of how this manifests in Quantum Computing can be seen if we apply two sequential Hadamard gates.  Skipping over the linear algebra and matrix multiplication, just know that if you input a |0〉to two sequential Hadamard gates, |0〉 is output 100% of the time (i.e., it is mathematically equivalent to the identity matrix).  However, if you measure the qubit between the two Hadamard gates, the output becomes a superposition that is |0〉 half of the time and |1〉 the other half of the time.  The mere act of “observing” the qubit between gates changes the outcome!  How does the qubit know it is being observed?

The Y-Gate and “Imaginary” Numbers

Nearly any “Intro to Quantum Mechanics” course, book or article, will mention the Stern-Gerlach experiment as one of the first topics.  It’s a fascinating subject that is well covered elsewhere, so I won’t provide much detail here (if interested in learning more, the Wikipedia post on the subject is a great intro and a link is included in the References at the end of this post).  The Stern–Gerlach experiment involves sending a beam of silver atoms through a  magnetic field and observing the deflection. The results show that particles possess an angular momentum that is similar to the angular momentum of a classically spinning object, but that it has only certain quantized values. Another important result is that only one component of a particle’s spin can be measured at one time, meaning that the measurement of the spin along the z-axis destroys information about a particle’s spin along the x and y axes. 

Now, if you’ll bear with me a bit as I reference linear algebra (don’t worry, you don’t need to understand linear algebra to appreciate this point), I want to highlight a very metaphysical aspect of this concept.  You’ll note below the matrix notation for two essential “gates” or basic QC functions.  The first is known as the “X-Gate” which is analogous to the “NOT” gate in classical computing.  If you apply a NOT gate in classical computing, it switches a 1 to a 0 or a 0 to a 1.  In Quantum Computing the X-Gate essentially flips the qubit on its head, also switching a |1〉to a |0〉or a |0〉to a |1〉.  This is straight forward only requiring the most basic familiarity with matrix multiplication to prove it.  However, the “Y-Gate” is quite different.  The Y-Gate essentially turns the qubit on its side, and its matrix representation is suddenly quite foreign.  The matrix representation of these two gates is shown below:

You will note for the Y-Gate the introduction of “i” (and -i) which is the symbol for the unfortunately named “imaginary” number.  “i” is mathematically defined as the solution to “X2 + 1 = 0.”  Although there is no “real” number that can solve this equation, it can still be used for certain mathematical functions.  It likely would be more fitting to call these “complex” numbers instead of imaginary.  Mathematicians would likely describe “i” as “lateral” or “perpendicular” to the plane where the “Real” number lay.   Evoking this concept of “Real” versus “Imaginary” suggests the imaginary numbers are surreal or mystical, and while that is itself a metaphysical concept, it is the fact that the information is quite different when orienting along the X-Axis versus orienting perpendicularly on the Y-Axis.  Again, for those familiar with linear algebra, this is rudimentary matrix multiplication and for those studying quantum physics, it is one of the first topics covered and proven by the Stern-Gerlach experiments back in the 1920’s.  The take-away for this post is that the same quantum “thing”, oriented in one direction, contains different information if you orient it in a perpendicular manner.

Back to the Beginning

As in the beginning of time.  That tiny fraction of an instant before the Big Bang.  It is generally believed that our current universe was preceded by a reality where everything (all energy and matter) was confined to an infinitesimally small point.  For reasons still largely unexplained, this super-concentrated point exploded and expanded into what is now the observable universe.  From apparent nothingness came a stupendously large amount of space, time, energy and matter.  Have you ever considered why that happened?  Surely many of you studied this as you learned about your religion, and largely consider it from a spiritual perspective.  But “something” led to the conversion of the pre-universe composition into the current universe comprised of matter and energy.  What force led some aspects of the original pinpoint to manifest as matter and some to manifest as energy?  Why isn’t it all “energy” or all “matter”?  I like to believe that “quantum” was the driving force even at this time-zero.   Let me explain.

Most introductory texts to quantum mechanics refers to the “uncertainty” principal.  It is referenced by Heisenberg in the context of never quite knowing both the speed and position of a particle, and also leads to QC calculations being probabilistic and not deterministic.  This is the concept Einstein was referring to in his famous “God doesn’t play dice…” quote.  Imagine for a moment that the original laws governing the Big Bang were completely deterministic.  In that case it would seem likely to me, that the universe would not today be made of various “stuff” but would rather be all of one thing. However, nothing interesting can be built from just one component, and certainly nothing organic.  So, the propensity of uncertainty may have led to the creation of energy and of matter of varying configurations which spurred a universe made of a dizzying array particles, forces, stars, planets, black holes and the other various wonders of nature. It’s this “quantum-ness” that allows for variability and it’s the variability that creates differing “things”.

Surfing Across Dimensions

This has just been a sampling of some of the head-scratching aspects of quantum and is intended to spur questions to contemplate as opposed to provide answers.  The mathematics which helps explain quantum mechanics, also govern the addition (or subtraction) of spatial dimensions, which also challenge our current world view.  Perhaps some of the remaining unanswered questions in quantum can be explained by action/forces in dimensions we cannot see?  Perhaps someone will come up with a “grand unified theory” to explain how the strong, weak, and electromagnetic forces all work and interact and how they relate to gravity, and perhaps that will help us understand these questions from an intuitive perspective.

In any case, despite the challenging mathematics, the non-intuitiveness of certain features, and the inability to definitively tie together all the disparate features of matter and energy, Quantum Computers continue to scale and to successfully run algorithms.  As these devices become more powerful, perhaps they will help uncover some of these mysteries.  In the meantime, I hope this post helps stimulate your wonder, and that you dig in deeper to learn and understand more.  I welcome your feedback and ponderings and you can reach me at russ@quantumtech.blog.

Disclosure: 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. 


References:

“Stern-Gerlach experiment.”  Wikipedia, Wikimedia Foundation, accessed June 16, 2022. Stern–Gerlach experiment – Wikipedia

Lewis, Peter J., Quantum Ontology – A Guide to the Metaphysics of Quantum Mechanics, Oxford University Press, 2016.

Greene, Brian, The Hidden Reality – Parallel Universes and the Deep Laws of the Cosmos, Vintage Books, 2011.

Image sources: www.ifunny.co (Bohr); @qphysicsmza (Feynman); www.theworldweekly.com (Einstein)

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.

A Review of ColdQuanta

A prior post entitled “Collaboration Dominates Quantum Computing” included an overview of ColdQuanta, a global quantum technology company building quantum computers, sensors, and related products.  This post provides additional information and details on its broad yet complementary business units. As ColdQuanta management relayed to me, ColdQuanta is “not just a Quantum Computing company,” it really is “a quantum technology company.” ColdQuanta’s strong history and momentum in quantum sensing, combined with its recently proven Quantum Computing capabilities, amounts to a powerful leader in broad quantum commercialization. This quantum platform focus supports an overall assessment of the likelihood of their success with a Rating Alpha = 0.95/Exceptional Performance Expected (see the Rating section for details). 

Background

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 temperature 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 and, importantly, current revenues. 

Core Technology

ColdQuanta, as its name implies, uses the quantum mechanics of “cold” atoms as its fundamental technology for a variety of important and compelling applications.  As Paul Lipman, President of Quantum Information Platforms for ColdQuanta conveyed to me:

“Atoms are nature’s perfect qubit.  Atoms are in and of themselves quantum objects.  And by cooling the atoms down we remove noise and we’re able to utilize their quantum nature for a variety of applications.  So, it’s one core technology, but with applications to compute, to quantum signal processing, to quantum sensing, to extremely sensitive quantum clocks.  We’re addressing a wide array of applications and use cases and technologies but with a single core underlying ‘qubit’ if you will.” 

Here is a brief summation of how they can create so many disparate devices from a single, core technology [Hemsoth, 2021]:

  1. Each device/application begins with a basic glass cell (see examples pictured below)
  2. The cell is evacuated with an ultra-high vacuum (UHV)
  3. It’s then filled with atoms of a single element
  4. Lasers are used to “trap” the individual atoms, which makes them cold, which in turn allows them to take on quantum properties
  5. Other lasers then arrange the atoms in specific configurations, depending on the application.  For example, a checkerboard-type arrangement is used to create qubits, counter-rotating atoms create gyroscopes and linear configurations can be used for accelerometers, etc.
  6. For quantum computing, additional lasers are used to further manipulate the atoms for computational purposes.

ColdQuanta uses this general configuration for two classes of products.  One, manifested in its “Albert” quantum matter design platform, is used for quantum sensing and related applications and the other, referred to as “Hilbert” is used for quantum computing.

Albert/Sensing Devices

ColdQuanta has been selling its various quantum sensing devices and components for many years, to notable customers like the Office of the Under Secretary of Defense for Research & Engineering (OUSD R&E) which awarded ColdQuanta a $1.8 million contract, the Defense Advanced Research Projects Agency (DARPA) which awarded a $3.6 million contract and a variety of UK government initiative awards totaling $3.5 million. 

In addition to selling quantum sensing products and components, ColdQuanta offers its “Albert” quantum matter design platform via cloud access.  Users of the beta platform can now remotely create and manipulate Bose-Einstein Condensate (BEC) on a quantum platform enabling them to control and arrange the quantum state of Albert to define its dynamic behavior and then capture and evaluate the results to accelerate research and refine designs.

“Albert” is the showcase quantum matter design platform ColdQuanta offers, encompassing its capabilities around quantum sensing.  The key to ColdQuanta’s ability to leverage its cold atom system for quantum sensing is rooted in two important properties, among others.  The first is the ability to place the individual atoms in a superposition (a fundamental quantum mechanical feature) and then measure the atoms to track ultra-minute changes and therefore “sense” various factors (i.e., time) with exquisite precision.  The other is when adding energy to the atoms and placing them into a Rydberg excitation, which in turn significantly increases the “size” of the configuration (the insertion of energy expands the outer orbit of the electrons thereby stretching or enlarging the overall size of the atoms) creating a tunable dipole.  This configuration is then extremely sensitive to radio frequency (RF) changes.

Examples of how users could create applications with Albert include the following:

Quantum sensing underpins a number of important products including:

  • Atomic Clocks
  • Sensors
  • Gyroscopes
  • Accelerometers
  • Gravimeters

These devices, in turn, enable or improve important applications such as:

  • GPS Resilience
  • Aircraft Guidance
  • Power Grids
  • Cell Towers
  • Financial Trading Systems
  • Autonomous Vehicles
  • Navigation Systems

ColdQuanta’s cold atom approach enables the creation of ruggedized, portable and compact systems.  In fact, they have successfully operated two different ColdQuanta systems on the International Space Station (ISS).   In addition to the UHV cells shown on the prior page, ColdQuanta has a broad quantum product offering including the following:

In fact, since its founding in 2007, ColdQuanta has been awarded over $60 million in contracts. Selling these components to other pioneers in the evolving quantum space, should provide meaningful and growing revenues, akin to the selling of “picks and shovels” to the early gold prospectors.

Hilbert

ColdQuanta recently released its cloud-based quantum computer called Hilbert which will reach 100 qubits.  Hilbert promises superior error correction, high qubit connectivity (starting at 4:1 but should quickly scale to 8:1 and ultimately closer to 100:1), long coherence times, and high gate fidelity, among other features.  In addition, because neutral atoms do not have an electrical charge, they can be packed close together making this method of qubit construction highly scalable and compact.  And most importantly, despite the super-cold atoms, the device itself operates at room temperature. 

As Paul Lipman noted in a recent press release on Hilbert, “the commercial release of Hilbert marks an important and exciting milestone for ColdQuanta and for the cold atom quantum computing modality.  Building on our recent world first in executing algorithms on a cold atom quantum computer, Hilbert demonstrates the power and scalability of atomic qubits and their promise to transform the quantum computing landscape.”

Hilbert supports the Qiskit API and will initially be available in beta to customers through ColdQuanta’s comprehensive multi-tenant cloud platform and soon via the Strangeworks Backstage Pass program (see below for additional details).  Integration with public cloud services is expected later this year.  The product roadmap calls for Hilbert to scale to 1,000 qubits by 2024 with the same strong connectivity, fidelity, and miniaturization at room temperature.

ColdQuanta Executive Management

ColdQuanta has over 150 employees including more than 90 physicists.  They have cross-border facilities with a distinct footprint in Boulder, Colorado (USA), Louisville, Kentucky (USA) Madison, Wisconsin (USA), Oxford (England), and now Chicago, Illinois (USA).  Senior management has deep and extensive experience in relevant quantum and technologically adjacent fields, and includes the following:

Scott Faris, CEO: Mr. Faris is an experienced technology company executive with over three decades of operating, venture-financing, and scaling experience including a diverse track record in new venture investment, technology company start-up and scaling operations, innovation process management, technology commercialization, corporate development and strategy, strategic alliances, and federal and commercial business development.  This background seems well matched with the stage and general capabilities of the Company.

Paul Lipman, President, Quantum Information Platforms: Mr. Lipman is an experienced leader in emerging technologies. Lipman is currently a Board Member at the Quantum Strategy Institute (QSI). Most recently, Lipman was CEO of BullGuard, a global leader in AI-driven cybersecurity, which was acquired by Avira/NortonLifeLock. His career experience includes the development of the world’s first IoT cybersecurity solution, Dojo (acquired by Forescout Technologies) as well as leading multiple innovative cybersecurity companies to successful exits. Prior to BullGuard, Lipman was CEO at SASE pioneer iSheriff (acquired by Mimecast). Earlier in his career, he held CEO, GM and executive leadership positions at Webroot, Keynote Systems, Total Defense and Accenture. Based in Silicon Valley, Lipman holds an MBA from Stanford and a bachelor’s degree in Physics from Manchester University in the UK.

Chester Kennedy, President, Research & Security Solutions: Mr. Kennedy has had a career focused on innovative technologies and their impacts on a variety of industries. Kennedy served as the Chief Executive Officer of BRIDG from 2015 to 2020, leveraging his aerospace and commercial electronics industries experience to lead the construction of a microelectronics fabrication facility and the establishment of a robust customer base.  Before BRIDG Kennedy spent 30 years at Lockheed Martin and its heritage organizations most recently as Vice President and Chief Engineer of Training and Logistics Solutions at Lockheed Martin Mission Systems and Training.  

Dana Anderson, Co-Founder and CTO: Dr. Anderson is co-founder and former CEO of ColdQuanta. He is a Fellow of JILA, and a Professor in the Dept. Of Physics and Electrical & Computer Engineering at the University of Colorado. He is also Director of the Quantum Applied Science and Engineering (QASE) at CU Boulder. Since 1993 he has been involved in guiding and manipulating cold and ultracold atoms. He and his collaborators Professor Carl Wieman and Dr. Eric Cornell (2001 Nobel Laureates in Physics) first demonstrated guiding of cold atoms through hollow core optical fibers in the mid-1990’s. Drs. Anderson and Cornell performed many of the earliest works guiding cold atoms on an “atom chip,” including the first demonstration of a chip-based atom Michelson interferometer. Professor Anderson’s group demonstrated the first ultracold atom chip portable vacuum system in 2004 and has been heavily involved in DoD-funded activities to develop ultracold atom chip. Dana received his Ph.D. from the University of Arizona and undergrad from Cornell.

Mark Saffman, Chief Scientist for Quantum Information: Dr. Mark Saffman, Professor of Physics at the University of Wisconsin-Madison is a preeminent expert in neutral atom quantum computing. He is an experimental physicist working in the areas of atomic physics, quantum and nonlinear optics, and quantum information processing. In 2010 his research team was the first to demonstrate a quantum CNOT gate and entanglement between two trapped neutral atom qubits. Mark has been recognized with an Alfred P. Sloan fellowship, the Vilas Associate Award from the University of Wisconsin-Madison and is a fellow of the American Physical Society and the Optical Society of America.  Mark worked as a Technical Staff Member at TRW Defense and Space Systems and subsequently as an Optical Engineer at Dantec Electronics Inc. in Denmark before going back to graduate school to earn his Ph.D. in Physics from the University of Colorado at Boulder.  Mark received his B.Sc. with honors in Applied Physics from the California Institute of Technology.

Competition

While there are a number of competitors in the quantum sensing space, most are early stage, less well capitalized or have a narrow product focus.  Certainly, some of these companies, such as Qnami (imaging and diagnostics), Innatera Nanosystems (medical sensors), Spiden (medical sensors) and QDTI (medical sensors), may develop strengths in a narrower field within quantum sensing, but none have the breadth of offering that ColdQuanta offers.  Within Quantum Computing specifically, there are a few neutral atom competitors including Atom Computing, Pasqal and QuEra.  Each of these QC competitors is also working towards using netural atoms as qubits, with Atom releasing its 100 qubit Phoenix system in July 2021, Pasqal’s 100 qubit machine coming soon to the Microsoft Azure platform, and QuEra’s planned for release on Amazon Braket later this year.  Since all these neutral atom quantum computers are extremely early in their release (or imminent), it is difficult to assess the feature and benefit differences among them.  Additionally, other forms of qubit, are already powering various quantum computers made by IBM, Rigetti, IonQ, Quantinuum, and others.  Each of these other qubit modalities have various strengths and weaknesses compared to the ColdQuanta structure so it will be interesting to follow the industry and see which platforms garner the strongest following and commercialization momentum. That said, in this early stage of QC, there are broad opportunities for early movers, including ColdQuanta, to gain traction and ultimately customers.

Funding

ColdQuanta has completed a series of investment rounds totaling $68.75 million so it has been well capitalized.  Their original funding in 2017 came via a $12 million grant from Small Business Innovation Research.  This was followed by a $6.75 million seed round in 2018, led by Maverick Ventures and joined by Global Frontier Investments (each receiving board seats).  Several additional grants and seed investments were received in 2019 and 2020 and a $32 million Series A round was completed in late 2020, with existing investors participating and joined by Foundry Group and Lennox Capital Partners. In 2021 a later stage venture round of $20 million was completed with GrayArch Partners and Wisconsin Alumni Research Foundation joining with the existing investors.  Overall, this represents a well-capitalized enterprise supported by some prominent names in venture investing.

Collaborations, Partnerships and a Recent Acquisition

In the prior post on Collaboration Dominating Quantum Computing, some of ColdQuanta’s various partnerships and collaborations were highlighted, so readers of that post may recognize some of the following details:

Super.tech

In May of this year, ColdQuanta announced the acquisition of software startup Super.tech, known for its innovations in quantum software.  Super.tech, co-founded by Pranav Gokhale and Fred Chong, is a member of the first cohort of the Duality incubator, run by the Chicago Quantum Exchange and the University of Chicago.   Super.tech has developed SuperstaQ which enables users to write quantum programs in any source language and target any quantum computer, providing API endpoints that enable deployment of quantum solvers for practical applications, without needing any quantum experience.  It has also created the SupermarQ suite of quantum computing benchmarks.  Super.tech will become the Chicago office of ColdQuanta and Gokhale, Chong and the other dozen or so Super.tech employees will join the ColdQuanta staff. 

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.

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.

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. 

Learning More

For quantum enthusiasts and investors seeking to learn more about ColdQuanta and their Albert and Hilbert platforms, I encourage you to visit their website and sign-up for updates. They are also highly 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 are also quite active on the social platforms including LinkedIn, Twitter, Facebook and YouTube and I encourage you to follow them on any or all those mediums.

For prospective customers interested in their sensing devices, it’s easy to create an Albert Beta Account here, or review some of their Albert resources and documentation here.   For more information about their various sensing products, they maintain product details here.   For details about their Hilbert universal and scalable Quantum Computing platform, visit here.

Summary

ColdQuanta has a solid team, protective IP, a highly regarded product portfolio, a strong balance sheet, and now a quantum computing platform.  It has diverse customers, legacy revenues, and should enjoy synergies both between its two broad “quantum” offerings (sensing and computing which both leverage neutral atom configurations) and now across its Quantum Computing hardware platform and recently acquired software platform.  A later start to Quantum Computing, and a broad geographic footprint provide a few modest headwinds.  The following table highlights some of the key attributes of ColdQuanta:

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 ColdQuanta including its strong IP, broad and complementary offering, and prestigious existing customers (and revenues), I am assigning the highest rating to ColdQuanta at this time, with an Alpha of 0.95 which equates to an “Exceptional performance expected.” When I began researching the Company, I had originally considered an evaluation one notch lower due to the non-availability at that time of their Hilbert Quantum Computer, but the release of the device provided the added impetus, in my view, to award this highest rating.

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:

Lipman, Paul. Interviewed by Russ Fein, April 27, 2022, and March 11, 2022.

Graham, T.M., Song, Y., Scott, J., et al, “Multi-qubit entanglement and algorithms on a neutral-atom quantum computer,” Nature, April 21, 2022.

Collaboration Between Leading Quantum Organizations Achieves Industry First for Neutral Atom Quantum Computing,” PRNewswire, April 20, 2022.

Hemsoth, Nicole, “One Company Could Own the Quantum Product Spectrum,” NextPlatform.com, February 1, 2021.

Hsin-Yuan (Robert) Huang Michael Blythe Broughton Jordan Cotler Sitan Chen Jerry Li Masoud Mohseni Hartmut Neven Ryan Babbush Richard Kueng John Preskill Jarrod Ryan McClean, “Quantum advantage in learning from experiments”, arXiv:2112.00778, December 3, 2021

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.

Why I’m Excited About the Classiq Coding Competition, and Why You Should be Too – Even if You Don’t Understand Quantum Programming

In a prior post entitled “The Case for an Annual ‘Quantum Games’ Competition”, I described how the amount of innovation and technical advancement in Quantum Computing (QC) has been incredible over the past 12 months or so, but how challenging it is to compare machine performance.  Should we focus on who has the “most qubits”? [hint, the answer is no]  Or highest “quantum volume”? Or which can run the longest before decoherence?  Or should we focus on #AQ as IonQ has suggested?  How about SupermarQ or QED-C proposed alternative benchmarks?

So, I suggested an annual “Quantum Games” or world Olympics to spur innovation and friendly competition. I volunteered to be a judge so that I could enjoy a front-row seat to watch the competitors give their best to the challenges.   I was thrilled that Classiq has since created the “Classiq Coding Competition” and honored to be a judge in this recently announced competition.   Let me explain why I’m so excited, but first some more details on the Competition.

Classiq’s Worldwide Competition – a $25,000 Challenge to Build the Best Quantum Circuits

As Nir Minerbi, the Classiq CEO noted in the Contest announcement, “Creating efficient quantum algorithms is part engineering, part art. The Classiq Coding Competition is a call to the world’s quantum software community to showcase their talents and demonstrate how quantum computing can take humans to new heights.  Efficient circuits enhance the ability of any quantum computer to solve important problems.”

Minerbi went on to add “You would be surprised how much can be achieved with compact, efficient circuits. The onboard computer used in the Apollo 11 space mission got a man to the moon using just 72 kilobytes of ROM. Quantum computing is taking off, and the need to create elegant and efficient quantum algorithms will exist for years to come. Organizations that manage to fit larger problems into available computers will reap their quantum benefits sooner than others. The Classiq Coding Competition will encourage the creativity and ingenuity required to make this happen and highlight the art of the possible (emphasis added) in compact, efficient circuits.”

The Competition includes four problems or challenges briefly described below:

ProblemGeneral DescriptionSuccess Metric
Log-Normal State PreparationMany quantum algorithms rely on initializing qubits in a specific state. The promised speedup of the algorithm depends on the ability to prepare the quantum state efficiently.Shortest circuit depth that provides an error below 0.01
Kakuro – A Constraint Satisfaction ProblemKakuro is a logic puzzle played on a grid of cells.  The challenge is to solve the puzzle using Grover’s algorithm.Minimized number of 2-qubit CX gates
Decomposing a Multi-Controlled Toffoli GateDecompose an MCX gate with 14 control qubits, one target qubit and up to five auxiliary qubits.Shortest circuit depth
Hamiltonian SimulationDescribes the evolution of molecules and solid-state systems by solving the Schrodinger equation. Quantum computers enable such simulation in a scalable manner.Using the CX and single qubit gates only with up to 10 total qubits, shortest circuit depth and an error below 0.1

While the descriptions in this chart will not resonate with readers unfamiliar with quantum algorithm construction, suffice it to say that it is a disparate and varied set of challenges with many possible solutions, which is part of the reason this is such an interesting challenge.

Here are a few more highlights of the Competition:

  • Submission Deadline: June 5, 2022 (by midnight US Eastern time)
  • Cash prizes of $25,000: with $3,000 to Gold medal, $1,500 to the Silver medal and $500 to the Bronze medal winner for each of the four problems.  There is also a $1,000 prize to each of two select winners in the “Youth” category (aged 18 and under) and $1,000 to each of the three most innovative solutions.
  • Multiple submissions are allowed
  • Submissions may rely on any preferred framework (i.e., Qiskit, Pennylane, etc.), however the Classiq platform may not but used
  • For those unable to receive a cash prize, Classiq has designated several worthy charities where the prize can be donated
  • A panel of five judges will review submissions
  • For more details, please visit the Classiq Competition page here

There has been significant and early enthusiasm for the Coding Competition with about 250 registrations received as of today, from all over the world (see map below) and more than 30 early solutions already submitted despite the contest being open for 12 more days.

Why is This Exciting for Non-Coders?

Nir’s quote about “highlighting the art of the possible” is why I’m so excited and why you should be too.  Some think of computer programs as rigid, dull instructions that either solve a problem or return an error.  However, the reality is that programming is a true art, and there are nuanced ways of sequencing, connecting and interweaving programming commands.  This is especially true with Quantum Computing in the early “NISQ” (noisy intermediate stage) environment where programmers need to deal with a few additional challenges besides simply programming the solution.  These include:

  • Building in error correction: Because of the noise inherent in current QCs, many of the available physical qubits must be used for error correction overhead.  Handling this in an efficient manner is quite challenging, especially with the limited numbers of qubits available today.
  • The probabilistic nature of quantum algorithms: Quantum programs or algorithms are not simply run once with the program returning an answer.  Quantum effects are given by probabilities and the algorithms are generally run many times; each time being referred to as a “shot”.  Often 10,000 shots are performed before an answer is determined.  The program needs to “reset” the qubits between shots to allow the next shot to begin.
  • Not all qubits are created equal: Certain QCs have limitations on which qubits can “entangle” with certain other qubits.  Some can only entangle with nearest neighbors and others are more flexible.  Some QCs have qubits that can maintain their state longer and so can run deeper algorithms.  In some instances, certain qubits within a given QC are less reliable than others, and there are other peculiarities among different QCs.  Programmers need to be able to adapt their programs to factor in these differing characteristics.
  • Machines and Development Kits vary: There are a number of different quantum computers available via the cloud and each has differing constraints and capabilities.  In addition, there are a variety of development kits or programming environments (i.e., Qiskit, Q#, Cirq, Strawberry Fields, Forest, etc.).

This is not meant to be a technical primer on programming QCs but rather is intended to showcase how much “art” there is in programming.  For those interested in learning more about these various constraints and challenges I encourage you to read Yuval Boger’s (Classiq’s CMO) excellent post on this topic here.

Quantum Computers are still quite early in their development.  Most available machines have limited numbers of qubits and various strengths and weaknesses regarding coherence, connectability and processing speed.  Ultimately, Quantum Computers will only be as valuable and impactful as the programs written for them, so seeing creativity and outside-the-box approaches to challenges with these early, noisy, faulty machines will be quite revealing.  I’m excited to get a glimpse into the varying approaches entrants use, and creative methods they employ, to overcome some of the challenges.

I look forward to reviewing the submissions in detail with my illustrious co-judges and plan to provide some insights about the competition in a future post.  In the meantime, I encourage you to enter the contest and/or tell your friends and colleagues about it.  It’s a great chance to test your skills, perhaps win some money, and most importantly, earn the bragging rights as a Medalist in the first ever Quantum Computing Coding Competition.

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, “Why Is It So Difficult to Write Software for Quantum Computers? And What Can Be Done About It”, Quantum Computing Report, accessed April 10, 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.

Quantum Computing is the “Manhattan Project” of This Generation

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.


References:

Nowakowski, Tomasz, “China’s ‘Micius’ Satellite Demonstrated Intercontinental Quantum Key Distribution for the First Time,” Spaceflight Insider, January 22, 2018.

O’Neill, Howard, “The US is worried that hackers are stealing data today so quantum computers can crack it in a decade,” MIT Technology Review, November 3, 2021.

Coker, James, “Security Teams Should Be Addressing Quantum Cyber-Threats Now,” InfoSecurity, April 26, 2022.

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

(Quantum) Winter is coming…or is it?

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.

  1. 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. 
  2. 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.
  3. 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”.
  4. 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.
  5. 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.
  6. 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.

IonQ Stock Falls After Short Report From Scorpion Capital,” May 4, 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.blogRuss Fein is a venture investor with deep interests in Quantum Computing (QC).  For more of his thoughts about QC please visit the link to the left.  For more information about his firm, please visit Corporate Fuel.  Russ can be reached at russ@quantumtech.blog.

Classiq – Company Evaluation

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 Rating Alpha = 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:

  1. Having tools to customize algorithms internally allows customers to build their own quantum software IP.  This is a subtle but crucial difference.
  2. 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.
  3. 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.

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

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

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

Siegelwax, Brian, “Demo: Classiq’s QAD Platform,” Medium: Level Up Coding, November 24, 2021.

Classiq Unveils Groundbreaking Capabilities for Its Quantum Algorithm Design Platform,” Businesswire, November 16, 2021.

Classiq to Collaborate with the Fraunhofer Institute and Other Leading Universities on New Methods for Industrial Quantum Use,” Businesswire, November 12, 2021.

Boger, Yuval, “Why Is It So Difficult to Write Software for Quantum Computers? And What Can Be Done About It”, Quantum Computing Report, accessed April 10, 2022.

Naveh, Amir, “Podcast with Amir Naveh, Co-Founder Classiq,” The Qubit Guy’s Podcast, February 16, 2022.

Swayne, Matt, “TQD Exclusive: A Classiq Solution to a Current Quantum Challenge,” The Quantum Insider, September 1, 2021.

Preimesberger, Chris J., “Quantum circuits automation gains attention and funding,” VentureBeat, February 21, 2022.

Shaw, David, “Quantum Software Outlook 2022,” Fact Based Insight, January 17, 2022.

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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.