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Quantum computing promises to revolutionize everything from machine learning to drug design – if we can build a computer with enough qubits (and fault-tolerance, which is for a different blog post). The issue of scaling is arguably one of the hardest problems in the field at large: how can we get more qubits, and critically, how can we make all those qubits work the way we need them to? 

A key issue in scaling is called the “wiring problem”. In general, one needs to send control signals to each qubit to perform the necessary operations required for a computation. All extant quantum computers have a hefty number of control signals being sent individually to each qubit. If nothing changes, then as one scales up the number of qubits they would also need to scale up the number of control signals in tandem. This isn’t just impractical (and prohibitively expensive), it also becomes quickly impossible - one can’t physically wire that many signals into a single chip, no matter how delicate their wiring is. The wiring problem is a general problem that all quantum computing companies face, and each architecture will need to find its own solution.

Another key issue in scaling is the “sorting problem” - essentially, you want to be able to move your qubits around so that they can “talk” to each other. While not strictly necessary (for example, superconducting architectures can’t do this), it allows for a much more flexible and robust design – it is the ability to move our qubits around that gives us “all-to-all connectivity”, which bestows a number of advantages such as access to ultra-efficient high density error correcting codes, low-error transversal gates, algorithms for simulating complex problems in physics and chemistry, and more. 

ҹɫֱ�� just put a huge dent in the scaling problem with their using a clever approach to minimize the number of signals needed to control the qubits, in a way that doesn’t scale prohibitively with the number of qubits. Specifically, the scheme uses a fixed number of (expensive) analog signals, independent of the number of qubits, plus a single digital input per qubit. Together, this is the minimum amount of information needed for complete motional control. All of this was done with a new trap chip arranged in a 2D grid, uniquely designed to have a perfect balance between the symmetry required to make a uniform trap with the capacity to break the symmetry in a way that gives “direction” (eg left vs right), all while allowing for efficient sorting compared to keeping qubits in a line or a loop. Taken together, this approach solves both the wiring and sorting problems – a remarkable achievement.

Stop-motion ion transport video showing loading an 8-site 2D grid trap with co-wiring and the swap-or-stay primitive operation. Single Yb ions are loaded off screen to the left, and are then transported into the grid top left site and shifted into place with the swap-or-stay primitive until the grid is fully populated. The stop-motion video was collected by segmenting the primitive operation and pausing mid-operation such that Yb fluorescence could be detected with a CMOS camera exposure.

Stop-motion ion transport video showing a chosen sorting operation implemented on an 8-site 2D grid trap with the swap-or-stay primitive. The sort is implemented by discrete choices of swaps or stays between neighboring sites. The numbers shown (indicated by dashed circles) at the beginning and end of the video show the initial and final location of the ions after the sort, e.g. the ion that starts at the top left site ends at the bottom right site. The stop-motion video was collected by segmenting the primitive operation and pausing mid-operation such that Yb fluorescence could be detected with a CMOS camera exposure.

“We are the first company that has designed a trap that can be run with a reasonable number of signals within a framework for a scalable architecture,” said Curtis Volin, Principal R&D Engineer and Scientist.

The team used this new approach to demonstrate qubit transport and sorting with impressive results; demonstrating a swap rate of 2.5 kHz and very low heating. The low heating highlights the quality of the control system, while the swap rate demonstrates the importance of a 2D grid layout – it is much quicker to rearrange qubits on a grid vs qubits in a line or loop. On top of all that, this demonstration was done on three completely separate systems, proving it is not just “hero data” that worked one time on one system, but is instead a reproducible, commercial-quality result. Further underscoring the reproducibility, the data was taken with both Strontium/Barium pairs and Ytterbium/Barium pairs. 

This demonstration is a powerful example of ҹɫֱ��’s commitment and capacity for the full design process from conception to delivery: our team designed a brand-new trap chip that has never been seen before, under strict engineering constraints, successfully fabricated that chip with exquisite quality, then finally demonstrated excellent experimental results on the new system. 

“It’s a heck of a demonstration,” quipped Ian Hoffman, a Lead Physicist at ҹɫֱ��.

The is the world’s largest physics conference enhancing education and collaboration in a variety of scientific research areas. As the interest in and potential of quantum technology increases, so does the number of conference sessions about the topic.

This year, the ҹɫֱ�� team will be participating in many of the APS March Meeting sessions to discuss the latest advancements in quantum technology. Find us throughout the week at the below sessions and visit us at Booth 605 in the .

Join these sessions to discover how ҹɫֱ�� is advancing quantum computing

(A51) Applications on Noisy Quantum Hardware I
‍Speaker: Kentaro Yamamoto, Senior Research Scientist
‍Date: Monday, March 4th
Time: 9:24 a.m. - 9:36 a.m. CST

(A40) Probing Structure and Dynamics with XUV and X-Ray Light: Ultrafast Studies of Photocatalysis and Water Radiolysis
Speaker: Evgeny Plekhanov, Quantum Physics Research Scientist
‍Date: Monday, March 4^th
‍Time: 10:00 a.m. - 10:12 a.m. CST

(G30) Commercial Applications of Quantum Computing
Speaker: Nathan Burdick, R&D Manager
Date: Tuesday, March 5^th
Time: 12:42 p.m. – 1:18 p.m. CST

(G56) Scaling Trapped Ion Quantum Computers
Speaker: Ian Hoffman, Lead Physicist
Date: Tuesday, March 5^th
Time: 12:06 p.m. – 12:42 p.m. CST

(K49) Algorithms and Implementations on Near-Term Quantum Computers
Speaker: Matthew DeCross, Advanced Physicist
Date: Tuesday, March 5^th
Time: 3:36 p.m. – 3:48 p.m. CST

(Q51) Co-evolution of Quantum and Classical Algorithms
‍Speaker: Cristina Cirstoiu, Quantum Research Scientist
‍Date: Wednesday, March 6thTime: 3:00 p.m. - 3:36 p.m. CST

(Q49) Quantum Algorithms for Many-Body Systems
‍Speaker: Maria Tudorovskaya, Research Scientist
‍Date: Wednesday, March 6^th
‍Time: 5:12 p.m. - 5:24 p.pm. CST

(Q14) Quantum Many-Body Scars and Related Phenomena
‍Speaker: Michael Schecter, Senior Advanced Physicist
Date: Wednesday. March 6th
Time: 5:24 p.m. – 5:36 p.m. CST

(S53) Trapped Ion Qubits
Speaker: Robert Tyler Sutherland, Lead Physicist
Date: Thursday, March 7^th
Time: 8:00 a.m. – 8:36 a.m. CST

(S53) Trapped Ion Qubits
Speaker: Andrew Schaffer, Advanced Physicist
Date: Thursday, March 7^th
Time: 8:48 a.m. – 9:00 a.m. CST

(S53) Trapped Ion Qubits
Speaker: Adam Reed, Senior Advanced Physicist
Date: Thursday, March 7^th
Time: 9:12 a.m. – 9:24 a.m. CST

(S53) Trapped Ion Qubits
Speaker: Lucas Sletten, Advanced Physicist
Date: Thursday, March 7^th
Time: 10:12 a.m. – 10:24 a.m. CST

(S51) Quantum Error Correction Code Performance and Implementation II
Speaker: Ben Criger, Senior Research Scientist
Date: Thursday, March 7^th
Time: 10:24 a.m. – 10:36 a.m. CST

(T50) Applications on Noisy Quantum Hardware II
Speaker: Eli Chertkov, Advanced Physicist
Date: Thursday, March 7^th
Time: 12:30 p.m. – 12:42 p.m. CST

(T50) Applications on Noisy Quantum Hardware II
Speaker: David Hayes, Senior R&D Manager
Date: Thursday, March 7^th
Time: 12:42 p.m. – 1:18 p.m. CST

Interested in a career at ҹɫֱ��? Meet our team at the Job Expo

Always on the leading edge of their fields, our hardware, software, sales, business, and operations teams are focused on personal, business, and technological growth. Curious, driven, and talented, our people are what makes ҹɫֱ�� tick. Every one of us is motivated to deliver on our mission to accelerate quantum computing. We are looking for team members with the same ambitions to join us!

Visit us at the to talk about positions at ҹɫֱ��.

How do machines “learn”? 

While recent years have seen incredible advancements in Artificial Intelligence (AI), no-one really knows how these ‘first-gen’ systems actually work. New work at ҹɫֱ�� is helping to develop different frameworks for AI that we can understand - making it interpretable and accountable and therefore far more fit for purpose. 

The current fascination with AI systems built around generative Large Language Models (LLMs) is entirely understandable, but lost amid the noise and excitement is the simple fact that AI tech in its current form is basically a “black box” that we can’t look into or examine in any meaningful manner. This is because when computer scientists were starting to figure out how to make machines ‘human like’ and ‘think’, they turned to our best model for a thinking machine, the human brain. The human brain essentially consists of neural networks, and so computer scientists developed artificial neural networks. 

However, just as we don’t fully understand how human intelligence works, it’s also true that we don’t really understand how current artificial intelligence works – neural networks are notoriously difficult to interpret and understand. This is broadly described as the “interpretability” issue in AI. 

It is self-evident that interpretability is crucial for all kinds of reasons – AI has the power to cause serious harm alongside immense good. It is critical that users understand why a system is making the decisions it does. When we read and hear about ‘safety concerns’ with AI systems, interpretability and accountability are key issues.

At ҹɫֱ�� we have been working on this issue for some time – and we began way before AI systems such as generative LLM’s became fashionable. In our AI team based out of Oxford, we have been focused on the development of frameworks for “compositional models” of artificial intelligence. Our intentions and aims are to build artificial intelligence that is interpretable and accountable. We do this in part by using a type of math called “category theory” that has been used in everything from classical computer programming to neuroscience.

Category theory has proven to be a sort of “Rosetta stone”, as John Baez put it, for understanding our universe in an expansive sense – category theory is helpful for things as seemingly disparate as physics and cognition. In a very general sense, categories represent things and ways to go between things, or in other words, a general science of systems and processes. Using this basic framework to understand cognition, we can build new artificial intelligences that are more useful to us – and we can build them on quantum computers, which promise remarkable computing power.

Our AI team, led by Dr. Stephen Clark, Head of AI at ҹɫֱ��, has published a applying these concepts to image recognition. They used their compositional quantum framework for cognition and AI to demonstrate how concepts like shape, color, size, and position can be learned by machines – including quantum computers.

“In the current environment with accountability and transparency being talked about in artificial intelligence, we have a body of research that really matters, and which will fundamentally affect the next generation of AI systems. This will happen sooner than many anticipate” said Ilyas Khan, ҹɫֱ��’s founder.

is part of a in quantum computing and artificial intelligence, which holds great promise for our future - as the authors say, “the advantages this may bring, especially with the advent of larger, fault-tolerant quantum computers in the future, is still being worked out by the research community, but the possibilities are intriguing at worst and transformational at best.”

the identity-first security solution for modern enterprises, and ҹɫֱ��, the world’s largest integrated quantum computing company, have partnered to strengthen the root of trust, a critical component in reliable public key infrastructures (PKIs) and code signing.

This integration is an important first step in a journey to protect Keyfactor’s users against multiple present-day and future cybersecurity risks, including the growing threat to encrypted communications posed by the potential misuse of rapidly advancing quantum computing technology.

Certainty About Key Quality

Given the rapid rise of bad actors, organizations are facing increasingly sophisticated attacks. In the future, misuse of quantum computing will be another threat that may compromise data. More than ever, data and communications rely on systems and processes to ensure their protection and accuracy. Digital certificates and PKI remain great options to strengthen the security of machine-to-machine communications from attacks.

Regardless of whether post-quantum or classical PKI algorithms are in use, the first step in the production of strong certificates is the generation of good-quality entropy, the random data used for the private keys. Traditionally, this has relied on noise derived from sources such as network and memory latency, as well as hardware assistance where the underlying system is able to provide it. Unfortunately, these approaches cannot guarantee the quality of the entropy, which leaves the strength of certificates against sophisticated attacks in doubt.

Verified quantum entropy sources solve this problem, using the laws of quantum physics to prove a near-perfect level of randomness in the entropy produced. With a high-quality entropy source, users can be confident that the keys they are using reflect the same level of quality and have not, in some way, been compromised in generation.

The Groundwork for Quantum Safety

To ensure high-quality keys, Keyfactor now offers a PKI platform that integrates with Quantum Origin, the world’s only verified source of quantum entropy.

Using verified quantum entropy assures the quality of keys used to provide the root of trust, both now for classical cryptography and in the future as post-quantum cryptographic algorithms also become more widely deployed.

“Quantum-readiness hinges on an organization’s knowledge of its cryptography and ability to defend itself against advanced threats. In this new era of cybersecurity, leaders are feeling a heightened sense of urgency to implement solutions that will secure digital interactions and communications before quantum computing becomes a reality,” said Joe Tong, Senior Vice President of Global Channel Sales, Keyfactor. “Keyfactor’s partnership with ҹɫֱ��, together with our existing collection of post-quantum algorithm implementations, will be able to provide customers with trust-based solutions that are hardened both with quantum technology and the latest post-quantum cryptographic research. Together with ҹɫֱ��, we are building strong cybersecurity foundations for the future, one step at a time.”

“The security and integrity of digital communications and transactions depends on the strength of digital certificates. By integrating Quantum Origin, Keyfactor’s customers can now leverage the world’s only source of verified quantum entropy to strengthen certificate generation.” said Duncan Jones, Head of Cybersecurity at ҹɫֱ��.

Quantum computing is a young, dynamic field – so young that the community is still exploring multiple different “architectures” for quantum computers. The computer “architecture” can roughly be described as what the computer is made out of – in other words, is it made out of superconductors or semiconductors? Are the qubits made from ions, superconducting “squids”, atoms, or even particles of light? We call these different physical realizations the “architecture” or “modality”.

Exploring the pros and cons of all the different modalities is an important part of current quantum computing research. Because ҹɫֱ�� is committed to the community, and even though our hardware is trapped-ion based, we often work in partnership with researchers exploring alternate options. This work allows us to both develop quantum technologies outside our own architecture while better developing our hardware-agnostic software.

Recently, our Oxford team has made big strides in our understanding of “photonic”, or light-based, quantum computing. First, they developed a string-diagram formalism for describing and optics. Then, they applied their formalism to solve outstanding problems in the field. 

The graphical approach made solving some problems in particular much easier than they would have been using more standard linear algebra techniques, in part because the circuits they are describing have a two-dimensional structure, just like the string diagrams themselves. By creating a diagrammatic representation of the circuits themselves, the researchers are more easily able to compute things such as the change in the circuit when a parameter is adjusted. 

In their , the team figured out how to take the derivative of (or “differentiate) linear optical circuits, which means they can now figure out how the circuit will change when certain parameters are adjusted. Differentiation is central to a whole class of algorithms (including optimization algorithms and any algorithm making use of “gradient descent”, which is a key component of many machine learning and AI techniques), making the teams’ results incredibly useful. This work will form the basis for an upcoming software platform for photonic quantum computing. 

In addition, this graphical approach to describing optical circuits is particularly advantageous for reasoning about multiple particles and composite quantum systems, like one must to understand fault-tolerance in quantum computing. While graphical languages are fairly new in the photonics sphere, they already seem to offer an insightful new perspective. Their current results open the door to “variational” approaches, which are used to solve things like combinatorial graph problems or problems in quantum chemistry.