AWS Quantum Technologies Blog AWS and JPMorganChase collaborate to advance quantum computing R&D 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. 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 our understanding of how quantum and classical resources can work together in practice. We developed a novel hybrid (quantum-classical) approach for solving large-scale graph optimization problems, validated and tested through experiments on Amazon Braket. We used quantum devices as co-processors to augment advanced classical solvers, benchmark results on systems available today on Amazon Braket, and shape future experiments. In this post, we share some highlights from our collaboration. You’ll learn how we: 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