Quantinuum Customer JPMorgan Chase Advances Constrained Quantum Optimization with New 20-Qubit System
Quantinuum H1-1 returns promising results on large-scale extractive summarization experiment
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 (Constrained Quantum Optimization for Extractive Summarization on a Trapped-ion Quantum Computer) 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 Quantinuum 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 Quantinuum’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 Quantinuum, 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 Quantinuum’s TKET 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.”
About Quantinuum
Quantinuum, the world’s largest integrated quantum company, pioneers powerful quantum computers and advanced software solutions. Quantinuum’s technology drives breakthroughs in materials discovery, cybersecurity, and next-gen quantum AI. With over 500 employees, including 370+ scientists and engineers, Quantinuum leads the quantum computing revolution across continents.
A team from Quantinuum and the University of Freiburg found that quantum computers outperform classical for a workhorse calculation often used in accelerators like the Large Hadron Collider (LHC) at CERN.
Quantinuum’s Ifan Williams worked with the University of Freiburg’s Mathieu Pellen to tackle a pernicious problem in accelerator physics: calculating “cross sections”. Together, they developed a general, scalable approach to calculating cross sections that offers a quadratic speed-up compared to its classical counterpart.
A “cross-section” relates to the probability of a certain interaction happening. Scientists who do experiments in particle accelerators compare real measurements with theoretical cross-section calculations (predictions), using the agreement (or disagreement) to reason about the nature of our universe.
Generally, scientists run Monte Carlo simulations to make their theoretical predictions. Monte Carlo simulations are currently the biggest computational bottleneck in experimental high-energy physics (HEP), costing enormous CPU resources, which will only grow larger as new experiments come online.
It’s hard to put a specific number on exactly how costly calculations like this are, but we can say that probing fundamental physics at the LHC probably uses roughly 10 billion CPUH/year for data treatment, simulations, and theory predictions. Knowing that the theory predictions represent approximately 15-25% of this total, putting even a 10% dent in this number would be a massive change.
The collaborators used Quantinuum’s Quantum Monte Carlo integration (QMCI) engine to solve the same problem. Their work is the first published general methodology for performing cross-section calculations in HEP using quantum integration.
Importantly, the team’s methodology is potentially extendable to the problem sizes needed for real-world HEP cross-section calculations (currently done classically). Overall, this work establishes a solid foundation for performing such computations on a quantum computer in the future.
The Large Hadron Collider, the world’s biggest particle accelerator, generates a billion collisions each second, far more data than can be computationally analyzed. Planned future experiments are expected to generate even more. Quantum computers are also accelerating. Quantinuum’s latest H2 System became the highest performing commercially available system in the world when it was launched. When it was upgraded in 2024, it became the first quantum computer that cannot be exactly simulated by any classical computer. Our next generation Helios, on schedule to launch in 2025, will encode at least a trillion times more information than the H2—this is the power of exponential growth.
We can’t wait to see what’s next with quantum computing and high-energy physics.
The Quantinuum team is looking forward to participating in this year’s SCAsia conference from March 10th – 13th in Singapore. Meet our team at Booth B2 to discover how Quantinuum is bridging the gap between quantum computing and high-performance compute with leading industry partners.
Our team will be participating in workshops and presenting at the keynote and plenary sessions to showcase our quantum computing technologies. Join us at the below sessions:
Monday, March 10th, 1:30 – 2:30pm
Workshop: Accelerating Quantum Supercomputing: CUDA-Q Tutorial across Multiple Quantum Platforms
Location: Room P10 – Peony Jr 4512 (Level 4)
This workshop will explore the seamless integration of classical and quantum resources for quantum-accelerated supercomputing. Join Kentaro Yamamoto and Enrico Rinaldi, Lead R&D Scientists at Quantinuum, for an Introduction to our integrated full-stack for quantum computing, Quantum Phase Estimation (QPE) for solving quantum chemistry problems, and a demonstration of a QPE algorithm with CUDA-Q on Quantinuum Systems. If you're interested in access to our quantum computers and emulator for use on the CUDA-Q platform, register here.
Tuesday, March 11th, 11:00 – 11:30pm
Keynote: Quantum Computing: A Transformative Force for Singapore's Regional Economy
Location: Melati Ballroom (Level 4)
Quantum Computing is no longer a distant promise; it has arrived and is poised to revolutionize several economies. Join our President and CEO, Dr. Rajeeb Hazra, to discover how Quantinuum’s approach to Quantum Generative AI is driving breakthroughs in applications which hold significant relevance for Singapore, in fields like chemistry, computational biology, and finance. Additionally, Raj will discuss the challenges and opportunities of adopting quantum solutions from both technical and business perspectives, emphasizing the importance of collaboration to build quantum applications that integrate the best of quantum and AI.
Tuesday, March 11th, 5:40 – 6:00pm
Industry Breakout Track: Transformative value of Quantum and AI: bringing meaningful insights for critical applications today
Location: Room L1 – Lotus Jr (Level 4)
The ability to solve classically intractable problems defines the transformative value of quantum computing, offering new tools to redefine industries and address complex humanity challenges. In this session with Dr. Elvira Shishenina, Senior Director of Strategic Initiatives, discover how Quantinuum’s hardware is leading the way in achieving early fault-tolerance, marking a significant step forward in computational capabilities. By integrating quantum technology with AI and high-performance computing, we are building systems designed to address real-world issues with efficiency, precision and scale. This approach empowers critical applications from hydrogen fuel cells and carbon capture to precision medicine, food security, and cybersecurity, providing meaningful insights at a commercial level today.
Wednesday, March 12th, 4:40 – 5:00pm
Hybrid Quantum Classical Computing Track: Quantifying Quantum Advantage with an End-to-End Quantum Algorithm for the Jones Polynomial
Location: Room O3 – Orchid Jr 4211-2 (Level 4)
Join Konstantinos Meichanetzidis, Head of Scientific Product Development, for this presentation on an end-to-end reconfigurable algorithmic pipeline for solving a famous problem in knot theory using a noisy digital quantum computer. Specifically, they estimate the value of the Jones polynomial at the fifth root of unity within additive error for any input link, i.e. a closed braid. This problem is DQC1-complete for Markov-closed braids and BQP-complete for Plat-closed braids, and we accommodate both versions of the problem. In their research, they demonstrate our quantum algorithm on Quantinuum’s H2 quantum computer and show the effect of problem-tailored error-mitigation techniques. Further, leveraging that the Jones polynomial is a link invariant, they construct an efficiently verifiable benchmark to characterize the effect of noise present in a given quantum processor. In parallel, they implement and benchmark the state-of-the-art tensor-network-based classical algorithms.The practical tools provided in the work presented will allow for precise resource estimation to identify near-term quantum advantage for a meaningful quantum-native problem in knot theory.
Thursday, March 13th, 11:00 – 11:30pm
Industry Plenary: Quantum Heuristics: From Worst Case to Practice
Location: Melati Ballroom (Level 4)
Which problems allow for a quantum speedup, and which do not? This question lies at the heart of quantum information processing. Providing a definitive answer is challenging, as it connects deeply to unresolved questions in complexity theory. To make progress, complexity theory relies on conjectures such as P≠NP and the Strong Exponential Time Hypothesis, which suggest that for many computational problems, we have discovered algorithms that are asymptotically close to optimal in the worst case. In this talk, Professor Harry Buhrman, Chief Scientist for Algorithms and Innovation, will explore the landscape from both theoretical and practical perspectives. On the theoretical side, I will introduce the concept of “queasy instances”—problem instances that are quantum-easy but classically hard (classically queasy). On the practical side, I will discuss how these insights connect to advancements in quantum hardware development and co-design.
*All times in Singapore Standard Time
BY HARRY BUHRMAN
Quantum computing continues to push the boundaries of what is computationally possible. A new study by Marcello Benedetti, Harry Buhrman, and Jordi Weggemans introduces Complement Sampling, a problem that highlights a dramatic separation between quantum and classical sample complexity. This work provides a robust demonstration of quantum advantage in a way that is not only provable but also feasible on near-term quantum devices.
The Complement Sampling Problem
Imagine a universe of N = 2n elements, from which a subset S of size K is drawn uniformly at random. The challenge is to sample from the complement S̅ without explicitly knowing S, but having access to samples of S. Classically, solving this problem requires roughly K samples, as the best a classical algorithm can do is guess at random after observing only some of the elements of S.
To better understand this, consider a small example. Suppose N = 8, meaning our universe consists of the numbers {0,1,2,3,4,5,6,7}. If a subset S of size K = 4 is drawn at random—say {1,3,5,7}—the goal is to sample from the complement S̅, which consists of {0,2,4,6}. A classical algorithm would need to collect and verify enough samples from S before it could infer what S̅ might be. However, a quantum algorithm can use a single superposition state over S (a quantum sample) to instantly generate a sample from S̅, eliminating the need for iterative searching.
Why This Matters: Quantum Advantage in Sample Complexity
Quantum advantage is often discussed in terms of computational speedups, such as those achieved by Shor’s algorithm for factoring large numbers. However, quantum resources provide advantages beyond time efficiency—they also affect how data is accessed, stored, and processed.
Complement Sampling fits into the category of sample complexity problems, where the goal is to minimize the number of samples needed to solve a problem. The authors prove that their quantum approach not only outperforms classical methods but does so in a way that is:
- Provable: It provides rigorous lower bounds on classical sample complexity, demonstrating an exponential separation.
- Verifiable: The correctness of the output of the sampler can be efficiently checked classically.
- NISQable: The quantum circuit required is shallow and feasible for Noisy Intermediate-Scale Quantum (NISQ) devices.
How the Quantum Algorithm Works
At its core, the quantum approach to Complement Sampling relies on the ability to perform a perfect swap between a subset S and its complement S̅. The method draws inspiration from a construction by Aaronson, Atia, and Susskind, which links state distinguishability to state swapping. The quantum algorithm:
- Uses a unitary transformation that maps the quantum sample |S⟩ to |S̅⟩ with high probability.
- For K = N/2, the algorithm works perfectly outputting an element from S̅ with probability 1.
- For other values of K, a probabilistic zero-error approach is used, ensuring correctness while reducing success probability.
This is made possible by quantum interference and superposition, allowing a quantum computer to manipulate distributions in ways that classical systems fundamentally cannot.
Classical Hardness and Cryptographic Implications
A crucial aspect of this work is its robustness. The authors prove that even for subsets generated using strong pseudorandom permutations, the problem remains hard for classical algorithms. This means that classical computers cannot efficiently solve Complement Sampling even with structured input distributions—an important consideration for real-world applications.
This robustness suggests potential applications in cryptography, where generating samples from complements could be useful in privacy-preserving protocols and quantum-secure verification methods.
Towards an Experimental Demonstration
Unlike some quantum advantage demonstrations that are difficult to verify classically (such as the random circuit sampling experiment), Complement Sampling is designed to be verifiable. The authors propose an interactive quantum versus classical game:
- A referee provides a quantum player with quantum samples from S.
- The player must return a sample from S̅
- A classical player, given the same number of classical samples, attempts to do the same.
While the classical player must resort to random guessing, the quantum player can leverage the swap algorithm to succeed with near certainty. Running such an experiment on NISQ hardware could serve as a practical demonstration of quantum advantage in a sample complexity setting.
Future Directions
This research raises exciting new questions:
- Can Complement Sampling be extended to more general probability distributions?
- Are there cryptographic protocols that can directly leverage this advantage?
- How well does the quantum algorithm perform in real-world noisy conditions?
With its blend of theoretical depth and experimental feasibility, Complement Sampling provides a compelling new frontier for demonstrating the power of quantum computing.
Conclusion
Complement Sampling represents one of the cleanest demonstrations of quantum advantage in a practical, verifiable, and NISQ-friendly setting. By leveraging quantum information processing in ways that classical computers fundamentally cannot, this work strengthens the case for near-term quantum technologies and their impact on computational complexity, cryptography, and beyond.
For those interested in the full details, the paper provides rigorous proofs, circuit designs, and further insights into the nature of quantum sample complexity. As quantum computing continues to evolve, Complement Sampling may serve as a cornerstone for future experimental demonstrations of quantum supremacy.
We have commenced work on the experiment – watch this space!