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Among other research, the Global Technology Applied Research (GTAR) Center at JPMorgan Chase is experimenting with quantum algorithms for constrained optimization to perform Natural Language Processing (NLP) for document summarization, addressing various application points across the firm. 

Marco Pistoia, Ph.D., Managing Director, Distinguished Engineer, and Head of GT Applied Research recently led the research effort around a constrained version of the Quantum Approximate Optimization Algorithm (QAOA) that can extract and summarize the most important information from legal documents and contracts. This work was recently published in Nature Scientific Reports () and deemed the “largest demonstration to date of constrained optimization on a gate-based quantum computer.” 

JPMorgan Chase was one of the early-access users of the ҹɫֱ�� H1-1 system when it was upgraded from 12 qubits with 3 parallel gating zones to 20 qubits with 5 parallel gating zones. The research team at JPMorgan Chase found the 20-qubit machine returned significantly better results than random guess without any error mitigation, despite the circuit depth exceeding 100 two-qubit gates. The circuits used were deeper than any quantum optimization circuits previously executed for any problem. “With 20 qubits, we could summarize bigger documents and the results were excellent,” Pistoia said. “We saw a difference, both in terms of the number of qubits and the quality of qubits.”

JPMorgan Chase has been working with ҹɫֱ��’s quantum hardware since 2020 (pre-merger) and Pistoia has seen the evolution of the machine over time, as companies raced to add qubits. “It was clear early on that the number of qubits doesn't matter,” he said. “In the short term, we need computers whose qubits are reliable and give us the results that we expect based on the reference values.”  

Jenni Strabley, Sr., Director of Offering Management for ҹɫֱ��, stated, “Quality counts when it comes to quantum computers. We know our users, like JPMC, expect that every time they use our H-Series quantum computers, they get the same, repeatable, high-quality performance. Quality isn’t typically part of the day-to-day conversation around quantum computers, but it needs to be for users like Marco and his team to progress in their research.”

More broadly, the researchers claimed that “this demonstration is a testament to the overall progress of quantum computing hardware. Our successful execution of complex circuits for constrained optimization depended heavily on all-to-all connectivity, as the circuit depth would have significantly increased if the circuit had to be compiled to a nearest-neighbor architecture.”

Describing the experiment 

The objective of the experiment was to produce a condensed text summary by selecting sentences verbatim from the original text. The specific goal was to maximize the centrality and minimize the redundancy of the sentences in the summary and do so with a limited number of sentences. 

The JPMorgan Chase researchers used all 20 qubits of the H1-1 and executed circuits with two-qubit gate depths of up to 159 and two-qubit gate counts of up to 765. The team used IBM’s Qiskit for circuit manipulation and noiseless simulation. For the hardware experiments, they used to optimize the circuits for H1-1’s native gate set. They also ran the quantum circuits in an emulator of the H1-1 device.

The JPMorgan Chase research team tested three algorithms: L-VQE, QAOA and XY-QAOA. L-VQE was easy to execute on the hardware but difficult to find good parameters for. Regarding the other two algorithms, it was easier to find good parameters, but the circuits were more expensive to execute. The XY-QAOA algorithm provided the best results. 

Looking ahead and across industries

Dr. Pistoia mentions that constrained optimization problems, such as extractive summarization, are ubiquitous in banks, thus finding high-quality solutions to constrained optimization problems can positively impact customers of all lines of business. It is also important to note that the optimization algorithm built for this experiment can also be used across other industries (e.g., transportation) because the underlying algorithm is the same in many cases.  

Even with the quality of the results from this extractive summarization work, the NLP algorithm is not ready to roll out just yet. “Quantum computers are not yet that powerful, but we're getting closer,” Pistoia said.  “These results demonstrate how algorithm and hardware progress is bringing the prospect of quantum advantage closer, which can be leveraged across many industries.”

The research team behind ҹɫֱ��'s state-of-the-art quantum computational chemistry platform, InQuanto, has demonstrated a new method that makes more efficient use of today's "noisy" quantum computers, for simulating chemical systems.

In a new paper, “Variational Phase Estimation with Variational Fast Forwarding”, , a team led by Nathan Fitzpatrick and co-authors Maria-Andreea Filip and David Muñoz Ramo, explored different methods and the trade-offs required to achieve results on near-term quantum hardware. The paper also assesses the hardware requirements for the proposed method.

Starting with the Variational Quantum Phase Estimation (VQPE) algorithm, commonly used to calculate molecular ground-state and excited state energies, the team combined it with variational fast-forwarding (VFF) to reduce the quantum circuit depth required to achieve good results. 

The demonstration made use of a , which can be used as a low-cost alternative to the traditional quantum phase estimation algorithm to estimate both the ground and excited-state energies of a quantum many-body system. The Krylov method uses time evolution to generate the subspace used in the algorithm, which can be very expensive in terms of gate depth. The new method demonstrated is less expensive, making the circuit depth required to achieve good results manageable.

The team decreased the circuit depth by using VFF, a hybrid classical-quantum algorithm, which provides an approximation to time-evolution, allowing VQPE to be applied with linear cost in the number of time-evolved states. Introducing VFF allows the time evolved states to be expressed with a lower fixed depth therefore the quantum computing resources required to run the algorithm are drastically decreased.

This new approach resulted in a circuit with a depth of 57 gates for the H₂ Molecule, of which 24 are CNOTs. This is a significant improvement from the original trotterized time-evolution implementation, particularly as the depth of this circuit remains constant for any number of steps. Whereas, the original trotterized circuit required 34 CNOTs per step, with a large number of steps required for high accuracy.

The techniques demonstrated in this paper will be of interest to quantum chemists seeking near-term results in fields such as, excited state quantum chemistry and strongly correlated materials.

The tradeoff involved in this use of VFF is that the results are more approximate. Improving this will be an area for future research.

It’s believed that quantum computing will transform the way we solve chemistry problems, and the ҹɫֱ�� scientific team continues to push the envelope towards making that a reality. 

In their latest research paper , ҹɫֱ�� scientists describe a new hybrid classical-quantum solver for chemistry. The method they developed can model complex molecules at a new level of efficiency and precision.

Dr. Michał Krompiec, Scientific Project Manager, and his colleague Dr. David Muñoz Ramo, Head of Quantum Chemistry, co-authored the paper,

The implications are significant as their innovation “tackles one of the biggest bottlenecks in modelling molecules on quantum computers,” according to Dr. Krompiec.

Quantum computers are a natural platform to solve chemistry problems. Chemical molecules are made of many interacting electrons, and quantum mechanics can describe the behavior and energies of these electrons. 

As Dr. Krompiec explains, “nature is not classical, it is quantum. We want to map the quantum system of interacting electrons into a quantum system of interacting qubits, and then solve it.” 

Solving the full picture of electron interactions is extremely difficult, but fortunately it is not always necessary. Scientists usually simplify the task by focusing on the active space of the molecule, a smaller subset of the problem which matters most. 

Even with these simplifications, difficulties remain. One challenge is carefully choosing this smaller subset, which describes strongly correlated electrons and is therefore more complex. Another challenge is accurately solving the rest of the system. Solving the chemistry of the complex subset can often be done from perturbation theory using so-called “multi-reference” methods.

In their work, the ҹɫֱ�� team came up with a new multi-reference technique. They maintain that only the strongly correlated part of the molecule should be calculated on a quantum computer. This is important, as this part usually scales exponentially with the size of the molecule, making it classically intractable. 

The quantum algorithm they used on this part relied on measuring reduced density matrices and feeding them into a multi-reference perturbation theory calculation, a combination that had never been used in this context. Implementing the quantum electronic structure solver on the active space and using measured reduced density matrices makes the problem less computationally expensive and the solution more accurate.

The team tested their workflow on two molecules - H₂ and Li₂ – using ҹɫֱ��’s hybrid solver implemented in the InQuanto quantum computational chemistry platform and IBM’s 27-qubit device. ҹɫֱ�� software is platform inclusive and is often tested on both its own H Series ion-trap quantum systems as well as others.

The non-strongly correlated regions of the molecules were run classically, as they would not benefit from a quantum speedup. The team’s results showed excellent agreement with previous models, meaning their method worked. Beyond that, the method showed great promise for reaching new levels of speed and accuracy for larger molecules. 

The future impact of this work could create a new paradigm to perform quantum chemistry. The authors of the paper believe it may represent the best way of computing dynamic correlation corrections to active space-type quantum methods. 

As Dr. Krompiec said, “Quantum chemistry can finally be solved with an application of a quantum solver. This can remove the factorial scaling which limits the applicability of this rigorous method to a very small subsystem.” 

The idea to use a multi-reference method along with reduced density matrix measurement is quite novel and stems from the diverse backgrounds of the team at ҹɫֱ��. It is a unique application of well-known quantum algorithms to a set of theoretical quantum chemistry problems. 

What’s Next

The use cases are vast. Analysis of catalyst and material properties may first benefit from this new method, which will have a tremendous impact in the automotive, aerospace, fine chemicals, semiconductor, and energy industries. 

Implementing this method on real hardware is limited by the current noise levels. But as the quality of the qubits increases, the method will unleash its full potential. ҹɫֱ��’s System Model H1 trapped-ion hardware, Powered by Honeywell, benefits from high fidelity qubits, and will be a valuable resource for quantum chemists wishing to follow this work. 

This hybrid quantum-classical method promises a path to quantum advantage for important chemistry problems, as machines become more powerful.

As Dr. Krompiec summarizes, “we haven’t just created a toy model that works for near-term devices. This is a fundamental method that will still be relevant as quantum computers continue to mature.”

By Duncan Jones

In September, nearly 200 senior cybersecurity leaders from around the world convened to discuss the state of U.S. cybersecurity at the. Topics around cybersecurity were varied and included discussions about moral asymmetry of today’s global threat actors, lessons learned from Ukraine and general discussions around all things that “keep us up at night” concerning cyber threats.

As a speaker at the Summit, I wanted to take a moment to share my take-aways from an important discussion that took place during our breakout session, “Future of Encryption: Moving to a Quantum Resistant World.” My esteemed fellow panelists from NSA, NIST, CMU and AWS exchanged insights as to where U.S. government agencies stand in their preparation for current and future threats to encryption, the likely hurdles they face, and the resources that exist to assist in the transition. Those responsible for moving their agency to a quantum-resistant world should find the following insights worth considering.

Getting Out of The Starting Gate

With the prospect of powerful quantum computers breaking known encryption methods on the horizon and with federal mandate now in place, the good news is that quantum-proof encryption is finally being discussed. The not-so-good-news is that it isn’t clear to cybersecurity practitioners what they need to do first. Understanding the threat is not nearly as difficult as understanding the timing, which seems to have left agency personnel at the starting gate of a planning process fraught with challenges – and urgency.

Why is the timeline so difficult to establish? Because there is no way of knowing when a quantum-based attack will take place. The Quantum-safe Security Working Group of the Cloud Security Alliance (CSA) chose the date, April 14, 2030, to represent “Y2Q,” also known as “Q-Day” – the moment secure IT infrastructure becomes vulnerable to the threat of a fault-tolerant quantum computer running Shor’s algorithm. The Biden Administration based its implementation timeline on the day that NIST announced the four winning algorithms for standardization. Then there is the “hack now, decrypt later” timeline which suggests that quantum-related attacks may already be underway.

Regardless of the final timeline or potential drivers, one thing that was clear to the panel attendees was that they need to start the transition now.

The Need to ‘Future-Proof’

I get this question often and was not disappointed when one attendee asked, “How can I convince my agency leadership that migrating to quantum-proof encryption is a priority when they are still trying to tackle basic cyber threats?”

The panelists responded and agreed that the U.S. government’s data storage requirements are unique in that classification dates are typically 20 years. This means that systems in development today, that are typically fielded over the next 10 years, will actually have a storage shelf life of 30 years minimum. Those systems need to be “future-proofed” today, a term that should be effective when trying to convince agency leaders of the priority.  

The need to future-proof is driven by a variety of scenarios, such as equipment and software upgrades. In general, it takes a long time (and perhaps even longer for government entities) to upgrade or change equipment, software, etc. It will take an extremely long time to update all of the software that has cryptography in place.

The panelists also agreed that given the extensive supply chain supporting federal systems, vendors are a critical component to the overall success of an agency’s future-proofing for the quantum age. In 10-15 years, there will be some government partner/vendor somewhere who will not have transitioned to quantum-proof encryption. For leaders who have not yet prioritized their agency’s cryptography migration, let them ponder that thought — and start to focus on the need to prepare.

Déjà Vu or Lessons Learned?

The panel shared several past technology migrations that were similar in their minds to the adoption of quantum computing.

Y2K was similar to the looming quantum threat by both the urgency and scale of the government’s need to migrate systems. However, without a deadline assigned to implementing the encryption migration, Y2K is really only similar in scale.

The panelists also recalled when every company had to hash function, but concluded that the amount of time, effort, and energy required to replace current encryption will be way more important than SHA-1 — and way more ubiquitous.

While previous technology migrations help to establish lessons learned for the government’s quantum-proof cryptography migration, the panel concluded that this go-round will have a very unique set of challenges — the likes of which organizations have never had to tackle before.

Where to Start

The consensus among panelists was that agencies need to first understand what data they have today and how vulnerable it is to attack. Data that is particularly sensitive, and vulnerable to the “hack-now, decrypt-later” attacks, should be prioritized above less sensitive data. For some organizations, this is a very challenging endeavor that they’ve never embarked upon before. Now is an opportune time to build inventory data and keep it up to date. From a planning and migration perspective, this is an agency’s chance to do it once and do it well.

It is important to assume from the start that the vast majority of organizations will need to migrate multiple times. Panelists emphasized the need for “crypto agility” that will enable future replacement of algorithms to be made easily. Crypto agility is about how easy it is to transition from one algorithm (or choice of parameters) to another. Organizations that prioritize long-term thinking should already be looking at this.

The panelists added that communicating with vendors early on in the planning process is vital.  As one panelist explained, “A lot of our service providers, vendors, etc. will be flipping switches for us, but a lot won’t. Understanding what your priorities are for flipping the switch and communicating it to your vendors is important.”

Help Is on Its Way

Matt Scholl of NIST shared about the is doing to provide guidance, tips, and to answer questions such as what are discovery tools and how do I budget? The project, announced in July 2022, is working to develop white papers, playbooks, demonstrations, tools that can help other organizations implement their conversions to post-quantum cryptography. Other resources that offer good guidance, according to Scholl, include recent , DHS’and the .

One additional resource that has been extremely helpful for our CISO customers is ҹɫֱ��’s The guide outlines what CISOs from any organization should be doing now and provides a basic transition roadmap to follow.

Conclusion

The discussion wrapped up with the acknowledgement that quantum has finally become part of the mainstream cybersecurity discussion and that the future benefit of quantum computing far outweighs the challenges of transitioning to new cryptography. As a parting thought, I emphasized the wonderful opportunity that agencies have to rethink how they do things and encouraged attendees to secure management commitment and funding for this much-needed modernization.

I want to give a special thanks to my fellow panelists for the engaging discussion: Margaret Salter, Director, Applied Cryptography, AWS, Dr. Mark Sherman, Director, Cybersecurity Foundations, CMU, Matthew Scholl, Chief of the Computer Security Division, ITL, NIST, and Dr. Adrian Stanger, Cybersecurity Directorate Senior Cryptographic Authority NSA.