Kortny Rolston-Duce, Director of Marketing Communications

In February, quantum researchers, developers and enthusiasts gathered online to listen to speakers, complete coding challenges, and compete in hackathons as part of QHack 2023.  

More than 2,800 people from 105 countries participated in this year’s event, which was organized by Xanadu and was part expo, hackathon, and scientific conference.  Since its inception in 2019, the multi-day QHack has become one of the largest online gatherings for the quantum computing community.

As part of its ongoing support for the growing quantum ecosystem, Atom Computing sponsored the visualization hackathon for QHack 2023.  A team from Qualition won the challenge after presenting a prototype for quantum image processing framework.

We met with Qualition CEO Alish Shamskhoozani, Chief Technology Officer Amirali Malekani Nezhad, and other members of the team to learn more about their organization, their winning project, and why they believe image processing is a killer application for quantum computing.

First off, tell us about Qualition.  Can you please describe what Qualition is and does?

Qualition was launched in 2022 by a group of researchers and developers located around the world with the common goal of advancing the nascent quantum computing industry and turning hype around this technology into reality.  Qualition, which is derived from “Quantum Coalition,” has expertise in a wide variety of quantum computing applications.

We take great pride in being mentored by Womanium, an organization dedicated to promoting diversity and inclusion in the field of quantum technology.  Moreover, we are honored to have technical ambassadors from some of the top quantum technology companies in the industry, including Quantinuum, qBraid, QWORLD, and CQTech. These partnerships provide us with the expertise and guidance of some of the most respected names in the field and enable us to collaborate on projects that have the potential to make a significant impact.

Whether through our research initiatives, our participation in quantum hackathons, or our partnerships with industry leaders, Qualition is committed to making a meaningful contribution to the advancement of quantum technology.

What project did your team complete for the QHack 2023 challenge?
Qualition presented a prototype for quantum image processing framework based on efficient quantum information encoding algorithms (A Distributed Amplitude Encoder, and A linearly scalable, FRQI inspired by the paper “Quantum pixel representations and compression for N-dimensional images'' by Mercy G. Amankwah, Dr. Daan Camps, Professor. E. Wes Bethel, Dr. Roel Van Beeumen & Dr. Talita Perciano) capable of tackling high dimensional data.  We demonstrated the features and performance of the framework through two branches of applications, namely Recognition, and Alteration use cases.

Recognition encompasses all classes of classification tasks, whereas Alteration encompasses all classes of filters, and provides a practical, fully-quantum framework for enhancing or processing images using techniques with similarities to traditional image processing. The two methods taken together can accommodate all traditional image processing algorithms in a fully quantum mechanical framework.

We presented a comprehensive analysis of a variety of pipeline options and assessed overall performance as a function of  the scale of the model, the fidelity of the encoder, and the accuracy of the trained model. This work serves as the foundation for Qualition’s Quantum Image Processing framework.

What were the team’s findings? Was there an advantage to using quantum computing?

Image processing was founded on the mathematics of linear algebra, where images are mathematically represented as vectors (similarly to quantum states), and operations applied to the images, also known as filters, are mathematically represented as matrices (similar to quantum operators).

Given the mathematical parallels between image processing and quantum mechanics, we consider image processing as a quantum evolution, and thus anticipate a significant advantage when performing the image processing tasks using quantum hardware. Quantum parallelism and presence of quantum Interference allow us to achieve a speedup when performing these tasks through a modeling of states and unitary operations. Assuming the near-term existence of more fault-tolerant and quantum hardware with higher quantum volumes, we can expect to tackle large classification tasks much faster than the equivalent classical paradigms.

The Recognition model is perhaps the most advantageous application of a quantum-enabled image processing model courtesy of the low dimensionality of the output, which is usually a label, and therefore easily extracted with a low number of shots. In contrast to the Recognition model, there exists an inherent challenge for the Alteration model. It requires the user to read out a large state vector, which in turn requires a considerably high number of shots for an adequately accurate demonstration.  As quantum computing hardware improves, however, so will the potential advantage of employing quantum filters characteristic of Alteration use cases to carry out  quantum image processing. Furthermore, our current research paves the way for development of feasible generative quantum AI models, something which we are striving for in the year to come!

In conclusion, we are proud to demonstrate initial evidencethat quantum image processing is a promising avenue for performing classification  for large models The current body of work at Qualition can be further extended to a variety of modalities and generalized to be the first of its kind, “High Dimensional QML”, feasible on current hardware. These pursuits  will be integral to demonstrating quantum hardware’s advantage for real-life industrial use cases over a vast array of fields, and certain aspects of the framework can be extended to other fields of quantum computing e.g., large-scale QAOA, more complex quantum drug simulation, and even faster quantum key distribution.

Why is image processing a strong application for quantum computing?

 Image processing is an inherently resource-intensive task due to its high dimensional nature. Classical paradigms have been performing quite well, as can be seen in a variety of papers and products for classification, alteration, and generations aspects of AI and machine learning. However, the computational cost of the models and the time taken to train them grows as we scale these models and may impact the environment due to the increased demands on classical computing hardware.

Given the mathematical similarities between image processing and quantum mechanics, we can demonstrate a significant advantage when performing the classification tasks using quantum hardware. Assuming we can feasibly implement the models on current hardware, we can expect significant speedups in training and inference time as compared to the equivalent classical models.

That said, image processing is perhaps the perfect use case for demonstrating the quantum advantage in industrial use-cases for the NISQ era hardware, for both scientific as well as artistic endeavors.

Why did Qualition select the visualization challenge?

Our Quantum Image Processing framework provides a user-friendly illustration for understanding the effect of quantum evolution operators on a given state, similar to that of a Bloch Circle, but generalized to N-dimensional states.

Given the mathematical correspondence, we can infer that real-valued quantum states can be represented as images, and thus we can visually observe the effect of applying any real-valued quantum operation to the state through the change in the state’s image representation. 

This visualization can be expanded to a procedural generation illustrating the gradual change in the quantum state by gradually applying the operation, i.e., applying an RY as we gradually increase the rotation angle.

What are some areas/industries in which quantum-enabled image processing could be used?
Quantum image processing can be applied to virtually any image processing task present in the industry, and some examples which would considerably benefit from such a quantum-enabled model would be medical Imaging (e.g., cancer detection, disease classification, segmentation for neurological diseases), satellite Imaging (e.g., object detection, land cover and land use detection, and change detection), and real-time unmanned vehicle routing (e.g., autonomous vehicles, unmanned drones, and robotics).

Quantum-enabled image processing obviates the need to remove certain features for a faster inferenc, a necessary step in classical Deep Learning models. Therefore, using quantum techniques, we can capture and retain more information. In the case of medical imaging, the ability to retain key information content may be the difference between finding a small tumor or not.

We look forward to seeing what the Qualition team does next!

Rob Hays, CEO

Today, researchers at Atom Computing released a pre-print publication on arXiv , demonstrating the ability to perform mid-circuit measurement on arbitrary qubits without disrupting others in an atomic array. The team applied mid-circuit measurement to detect the loss of qubits from the array (a well-known anticipated error), and successfully replaced the lost qubits from a nearby reservoir.

Path to Fault Tolerance

At Atom Computing, we believe the true potential of quantum computing will be achieved when devices are capable of fault-tolerant computation.  Our company’s focus is on leading that race to unlock the enormous potential of quantum computing applications for industrial and scientific uses. 

Dr. John Preskill, famed theoretical physics professor at California Institute of Technology, who coined the phrase Noisy Intermediate Scale Quantum (NISQ) to describe the current stage of quantum computing, said it best in 2018: “We must not lose sight of the essential longer-term goal: hastening the onset of the fault tolerant era.”

It will likely require hundreds of thousands or millions of physical qubits to achieve fault tolerant systems that can operate continuously and deliver accurate results, overcoming any errors that may occur during computation just as classical computing systems do.  Mid-circuit measurement is one of several key building blocks required to achieve fault tolerant systems:

• Large numbers of physical qubits
• Long coherence times
• High gate fidelities
• Mid-circuit measurement
• Detection and correction of errors in real time
• Algorithm and controls to create logical qubits

Mid-circuit measurement has been demonstrated on superconducting and trapped-ion quantum technologies.  Atom Computing, however, is the first company to do so on a commercial atomic array system.  Recent achievements in the neutral atom research community have shown that atomic arrays are emerging from their “dark horse” status to a preferred architecture with intriguing potential.

Importance of Mid-Circuit Measurement

In quantum computing, circuits act as instructions that tell a quantum computer how to perform a calculation.  Circuits define how the programmer intends for the qubits to interact, which gates they need to complete, and in what order they need to be performed.

Mid-circuit measurement involves probing the quantum state of certain qubits, known as ancillas, without disrupting nearby data qubits that perform calculations.  The ability to measure or read out specific qubits during computation without disrupting the rest is essential for quantum developers.  It enables them to glimpse inside a calculation and use conditional branching to determine which action to take based on results of the measurement, similar to IF/THEN statements used in classical computing. With this capability errors can be detected, identified, and corrected in real time. 

Dr. Ben Bloom, Atom Computing Founder and Chief Technology Officer, called this demonstration an important step for the company’s technology, which uses lasers to hold neutral atom qubits in a two-dimensional array to perform computations.

“This is further proof that atomic array quantum computers are rapidly gaining ground in the race to build large-scale, fault-tolerant quantum computers,” Bloom said. “Mid-circuit measurement enables us to understand what is happening during a computation and make decisions based on the information we are seeing,”

Doing this is tricky. Qubits, whether they are in an atomic array, ion trap, or on a chip, are situated microscopically close. Qubits are finicky, fragile, and sensitive.  A stray photon from a laser or a stray electric field can cause the wrong qubit to decohere and lose its quantum state. 

The Atom Computing team exhibited a technique to “hide” data qubits and shield them from the laser used to measure ancillas without losing any of the quantum information stored in the data qubits. They also showed a competitive SPAM fidelity, a metric that states how well a qubit can be read out. This work demonstrates an important pathway to continuous circuit processing.

What’s Next

Atom Computing is building quantum computers from arrays of neutral atoms because of the potential to significantly scale qubit numbers with each generation.  We previously demonstrated record coherence times on our 100-qubit prototype system and are now working on larger scale production systems to offer as a commercial cloud service.  Our demonstration of mid-circuit measurement, error detection, and correction was performed on these next-generation systems.

Our journey toward fault tolerance continues. We are working to achieve all the necessary “ingredients” listed above on our current systems and on future machines with the Defense Advanced Research Projects Agency.  DARPA selected Atom Computing to explore how atomic arrays of neutral atoms can accelerate the path to fault-tolerant quantum computing.

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Dr. Jonathan King, Chief Scientist and Co-Founder

I am pleased to welcome two renowned researchers in the field of quantum information science to Atom Computing as independent scientific advisors. 

Dr. Bert de Jong, Senior Scientist and Deputy Director of the Quantum Systems Accelerator at Lawrence Berkeley National Laboratory, and Dr. Eliot Kapit, Associate Professor of Physics and Director of Quantum Engineering at Colorado School of Mines, join our long-time advisor Dr. Jun Ye, Professor of Physics at University of Colorado-Boulder and Fellow at JILA and the National Institutes of Standards and Technology.  Together, these scientists and academic leaders will help us advance the state of the art in quantum computing by providing deep technical expertise and guidance to our team. 

Since its inception in 2018, Atom Computing has been building quantum computers from atomic arrays of optically trapped neutral atoms with the goal of delivering large-scale fault-tolerant quantum computing for a range of use cases and applications.  Building high-quality quantum computers is hard work that requires tight collaboration across our team of theoretical and experimental physicists and engineers of multiple disciplines. 

We also frequently consult with researchers from other organizations to solve difficult technical challenges.   In addition to our scientific advisors, we also have active collaborations with other experts from Sandia National Laboratories, DARPA, University of Washington, University of Colorado-Boulder, and others to support R&D of technologies required for our future roadmap.

We are at the point where we are focusing not only on developing our hardware, but also what users can do with it to solve commercial problems at scale.  Bert and Eliot are experts in quantum computing use cases and algorithms.  Their expertise will help our quantum applications team and customers learn how to get the most value out of our hardware platforms.

Bert leads Lawrence Berkeley National Laboratory’s Computational Sciences department and its Applied Computing for Scientific Discovery Group. His group’s research encompasses exascale computing, quantum computing and AI for scientific discovery.  (View his bio.) 

When asked about the role, Bert stated, "Atom Computing's path to universal quantum computing with atom arrays is exciting, and I am honored to be a scientific advisor providing critical input and guidance into their software and algorithm strategies.”

Eliot’s research at Colorado School of Mines focuses on quantum information science, particularly routes to achieve practical and scalable quantum advantage in noisy, near-term hardware.  (View his bio). 

Here is what Eliot said about his new role: “I'm excited to have joined the scientific advisory board at Atom Computing. Neutral atom quantum computing has progressed shockingly quickly in just the past few years - and I say this as someone who's been working on superconducting qubits for the past decade, which certainly haven't been standing still.  I think Atom, in particular, has both a compelling roadmap toward large scale quantum computers, and a very capable team to make it happen.”

I am looking forward to future collaborations with Bert, Eliot, and Jun to drive large-scale quantum computing with applications that change the world.

In recent months, researchers from different institutions won major physics awards for advancing optical tweezer arrays and their use in quantum information sciences.

These announcements drew broader attention to optical tweezer arrays, even in the physics community.  At Atom Computing, however, they are always top-of-mind – optical tweezers are critical to our atomic array quantum computing technology. 

What are optical tweezer arrays and how and why do we use them in our quantum computers? Dr. Remy Notermans, who helped develop the optical tweezer array for Phoenix, our prototype system, answers these questions and more.

What are optical tweezer arrays?
A single optical tweezer is a beam of light used to capture atoms, molecules, cells, or nanoparticles, hold them in place or move them as needed. 

This is possible because light can attract or repulse a particle depending on the color (wavelength) of the light and the absorption properties (electronic energy level structure) of the particle.  By choosing the right wavelength, a particle will be drawn or attracted to the region with the highest intensity of light, trapped in what is known as a potential well (the energy landscape in which an atom wants to go to the lowest point.)

An optical tweezer is created when a laser beam is focused through a microscope objective lens. As the laser beam gets focused it forms into a "tweezer" capable of holding miniscule objects and manipulating them in its focal point. Think of the tractor beam from Star Trek.

To create an optical tweezer array, the laser beam is manipulated before it is focused through a microscope object lens to create a custom-made array of optical tweezers that can be tailored to specific needs – topology, dimensions, and orientation.

Are optical tweezer arrays a new technology?
Optical tweezers have been used by researchers in the fields of medicine, genetics, and chemistry for decades. In fact, Arthur Ashkin, “the father of optical tweezers,” was awarded the Nobel Prize in Physics in 2018. Ashkin’s work dates to 1970 when he first detected optical scattering and the effect of different levels of force on particles the size of microns.  He and some of his colleagues later observed a focused beam of light holding tiny particles in place – or optical tweezers.

More recent scientific work has expanded to actual arrays of optical tweezers, allowing for studying many particles simultaneously, biophysics research, and of course quantum information processing.

How does Atom Computing use optical tweezer arrays? What are the benefits?
Optical tweezers are critical to our atomic array quantum computing technology, which uses neutral atoms as qubits.  We reflect a laser beam off a spatial light modulator to create an array of many optical tweezers that each “trap” an individual qubit.  For example, Phoenix, our 100-qubit prototype quantum computer, has more than 200 optical tweezers created from a single laser. Each tweezer can be individually calibrated and optimized to ensure precise control. 

Optical tweezer arrays enable us to fit many qubits in a very small amount of space, which means that scaling the number of qubits by orders of magnitude does not significantly change the size of our quantum processing unit.  By integrating clever optical designs, we foresee a sustainable path toward atomic arrays that are large enough for fault-tolerant quantum computing.

In fact, optical tweezers inspired the Atom Computing logo.  If you turn our “A” logo upside down, it is a visual representation of an optical tweezer holding an atom in a potential well.

Are optical tweezer arrays used for other purposes?
Yes, optical tweezer arrays have been used extensively by researchers in other scientific fields. They have been used by scientists to trap living cells, viruses, bacteria, molecules, and even DNA strands so they can be studied.

Has the work of the New Horizons Physics Prize winners influenced Atom Computing’s approach? If so, how? 
We understand this is a fundamental part of the academic-industrial ecosystem, which is why Atom Computing is involved with many partnerships and funds academic research efforts that potentially help us propel our technology forward.  Combined with the knowledge and experience of our world-class engineering teams, we take these breakthroughs to the next level in terms of scalability, robustness, and systems integration.