# JPMorganChase and AWS Showcase Hybrid Quantum Tools for Large-Scale Optimization — retained record > Retained news record from news:hpcwire. Canonical: https://highsignal.app/data/records/91df6179a2d026c1 [← sources](https://highsignal.app/data) source: [news](https://highsignal.app/data/news) (news:hpcwire) published 2026-08-06 14:39 ingested 2026-08-07 07:26 [original source ↗](https://www.hpcwire.com/off-the-wire/jpmorganchase-and-aws-showcase-hybrid-quantum-tools-for-large-scale-optimization/) ## retained text ``` Off the Wire Press Releases This post was contributed by Martin Schuetz and Ruben Andrist from the Amazon Advanced Solutions Lab, and Romina Yalovetzky and Atithi Acharya from Global Technology Applied Research at JPMorganChase. Aug. 6, 2026 — Quantum researchers at JPMorganChase and the Amazon Advanced Solutions Lab are working together to explore how quantum technologies may help address complex optimization problems in finance and beyond. This sustained, multi-project research program has enabled shared methodologies and co-designed algorithms and experiments, deepening understanding of how quantum and classical resources can work together in practice. The researchers developed a novel hybrid (quantum-classical) approach for solving large-scale graph optimization problems, validated and tested through experiments on Amazon Braket. They used quantum devices as co-processors to augment advanced classical solvers, benchmark results on systems available today on Amazon Braket, and shape future experiments. This post will share some highlights from the collaboration. You’ll learn how the researchers: 1. Built a decomposition pipeline that reduces portfolio optimization problems by ~80%, making them small enough for near-term quantum hardware. 2. Developed a compilation toolkit that has the potential to shrink qubit requirements by orders of magnitude for real-world graph problems. 3. Created qReduMIS, a hybrid algorithm where quantum devices serve as co-processors to classical solvers – achieving above ~89% average success rates on hard problem instances using QuEra’s Aquila device on Amazon Braket. A Suite of Tools for Near-Term Quantum Hardware Combinatorial optimization problems are ubiquitous across different areas in industry and science, with prominent examples in areas like transportation and logistics, telecommunications, manufacturing, and finance. Analog neutral-atom quantum machines based on Rydberg atoms provide a novel platform to design and implement quantum optimization algorithms, with scientists in both industry and academia searching for the most promising types of problems for which an early quantum advantage could be demonstrated. In previous work the researchers studied the computational hardness of certain optimization problems, inspired by real-world use cases in finance (see the blog post here). With a deeper understanding for problem hardness and quantum speedups, the collaboration has then produced three additional research papers that together provide a comprehensive toolkit for solving large-scale optimization problems on near-term Rydberg quantum hardware. An overview of the results are provided here – for more details see the original publications in Refs. [1-3]. Decomposition Pipeline for Portfolio Optimization [1] The first paper [1] addresses the challenge of solving industrially relevant constrained optimization problems, such as portfolio optimization and rebalancing, which are often intractable or difficult to solve exactly. The researchers propose and benchmark a decomposition pipeline that can break large-scale problems down into smaller, more manageable subproblems, with implications for both classical and quantum computation. See Figure 1 for a schematic illustration. The researchers’ empirical results show that their pipeline consistently reduces the size of real-world portfolio optimization problems by approximately 80%, while maintaining solution quality within a minimal error. The researchers further report on a 3x reduction in time-to-solution for large-scale portfolio optimization problems with up to 1500 variables. The pipeline decomposes the optimization problem into constrained subproblems, which are then solved separately and aggregated to give a final result. By leveraging the underlying structure present in financial data and using random matrix theory to clean correlation matrices, researchers can use modified community detection algorithms to identify subproblems. Moreover, by decomposing large problems into several smaller subproblems, the pipeline allows researchers to embed these smaller subproblems onto near-term quantum devices with hundreds of qubits, thus enabling the use of near-term quantum devices as solvers, and providing a path toward practical utility of quantum computers in portfolio optimization. Quantum Compilation Toolkit for Rydberg Atom Arrays [2] The second paper [2] proposes and implements a comprehensive quantum compilation toolkit for solving the maximum independent set (MIS) problem on quantum hardware based on Rydberg atom arrays. The end-to-end pipeline involves three core components to efficiently map generic MIS instances onto Rydberg arrays with unit-disk connectivity, with modules for graph reduction, hardware compatibility checks, and graph embedding; see Figure 2 for illustration. The first module (reducer) provides hardware-agnostic and deterministic reduction logic that iteratively reduces the problem size via (exact) lazy clique removals. Real-world networks can typically be reduced by orders of magnitude on subsecond timescales, thus significantly cutting down the eventual load for quantum devices. For example, consider the real-world Cora citation graph with approximately 2,700 nodes. To embed this graph on Rydberg hardware using previous methods would require approximately 29 million qubits, far beyond the capabilities of near-term quantum hardware. Leveraging exact graph reduction techniques together with an optimized embedding scheme, the researchers show that this graph can in fact be encoded with tens of qubits, thus reducing the required qubit overhead by orders of magnitude and enabling the use of near-term quantum devices. The second module (compatibility checker) implements a hardware compatibility checker that quickly determines whether a given input graph may be compatible with the restrictions imposed by Rydberg quantum hardware. The third module (embedder) describes hardware-efficient graph embedding routines to generate approximate encodings with controllable overhead and optimized ancilla placements. qReduMIS: A Quantum-Informed Reduction Algorithm [3] The third paper [3] introduces qReduMIS, a hybrid quantum-classical algorithm for the maximum independent set (MIS) problem. Here, the researchers use a quantum computer as a co-processor that feeds information back into classical reduction logic. Specifically, quantum measurement data identifies frozen vertices that are highly likely (or unlikely) to appear in large independent sets. Removing these vertices unblocks further classical kernelization, creating a recursive loop where quantum and classical resources reinforce each other. See Figure 3 for a schematic illustration of the qReduMIS framework. Researchers systematically assess the performance of qReduMIS based on experiments with up to 231 qubits run on QuEra’s Aquila quantum device available through Amazon Braket. The results are displayed in Figure 4. For quantum annealing algorithms (QAA) the success rate PMIS is strongly suppressed for hard instances, dropping to small PMIS ≳ 0 for most (hard) instances with hardness H ≳ 10. While more performant than QAA, researchers observe a similar performance drop for classical simulated annealing (SA) [4]. Conversely, qReduMIS solves most instances to optimality with PMIS = 1 and maintains a non-zero success rate with average PMIS ≳ 89% throughout the testbed. Overall, these results illustrate the benefits of introducing kernelization techniques over both classical and quantum Markov-chain Monte Carlo-based methods. The experiments show that qReduMIS can help address fundamental performance limitations faced by a broad set of (quantum) solvers including Rydberg quantum devices, given by the exponential performance suppression in the conductance-like hardness parameter for hard MIS problem instances. Looking Ahead Together, these three papers provide a comprehensive suite of tools that extends the class of problems that can be tackled with near-term Rydberg atom arrays and help push the art of the possible for Rydberg quantum devices (as available on Amazon Braket through the QuEra Aquila device). These results should help identify the most promising directions for applications of Rydberg devices and direct the community’s ongoing efforts towards quantum advantage, hopefully inspiring many interesting future experiments. References [1] A. Acharya, R. Yalovetzky, P. Minssen, S. Chakrabarti, R. Shaydulin, R. Raymond, Y. Sun, D. Herman, R. S. Andrist, G. Salton, M. J. A. Schuetz, H. G. Katzgraber, and M. Pistoia, Decomposition Pipeline for Large-Scale Portfolio Optimization with Applications to Near-Term Quantum Computing, Phys. Rev. Research 7, 023142 (2025); arXiv:2409.10301. [2] M. J. A. Schuetz, R. S. Andrist, G. Salton, R. Yalovetzky, R. Raymond, Y. Sun, A. Acharya, S. Chakrabarti, M. Pistoia, and H. G. Katzgraber, Quantum Compilation Toolkit for Rydberg Atom Arrays with Implications for Problem Hardness and Quantum Speedups, Phys. Rev. Research 7, 033107 (2025); arXiv:2412.14976. [3] M. J. A. Schuetz, R. Yalovetzky, R. S. Andrist, G. Salton, Y. Sun, R. Raymond, S. Chakrabarti, A. Acharya, R. Shaydulin, M. Pistoia, and H. G. Katzgraber, qReduMIS: A Quantum-Informed Reduction Algorithm for the Maximum Independent Set Problem, arXiv:2503.12551. [4] R. S. Andrist, M. J. A. Schuetz, P. Minssen, R. Yalovetzky, S. Chakrabarti, D. Herman, N. Kumar, G. Salton, R. Shaydulin, Y. Sun, M. Pistoia, and H. G. Katzgraber, Hardness of the Maximum Independent Set Problem on Unit-Disk Graphs and Prospects for Quantum Speedups, Phys. Rev. Research 5, 043277 (2023); arXiv:2307.09442. Source: Martin Schuetz, Atithi Acharya, Romina Yalovetzky, and Ruben Andrist; Amazon and JPMorganChase; AWS ``` document fetched 2026-08-07 07:26 · canonical https://hpcwire.com/off-the-wire/jpmorganchase-and-aws-showcase-hybrid-quantum-tools-for-large-scale-optimization