JPMorgan Chase and the Amazon Advanced Solutions Lab announced today a joint research program aimed at applying quantum technologies to complex optimization problems in finance. The collaboration has produced three research papers that introduce new tools for solving large‑scale optimization tasks on near‑term Rydberg quantum hardware, positioning quantum devices as co‑processors alongside classical solvers. The first paper tackles portfolio optimization and rebalancing, presenting a decomposition pipeline that shrinks real‑world portfolio problems by roughly 80% while preserving solution quality, and delivers a three‑fold reduction in time‑to‑solution for instances containing up to 1,500 variables. The second paper introduces a quantum compilation toolkit for the maximum independent set problem on Rydberg atom arrays; it dramatically lowers qubit requirements, exemplified by the Cora citation graph (≈2,700 nodes) whose earlier methods demanded about 29 million qubits, now reduced to merely tens of qubits. The third paper describes qReduMIS, a hybrid quantum‑classical algorithm that combines exact polynomial‑time reduction logic with quantum measurement data, achieving an average success rate exceeding 89% on hard problem instances when run on QuEra’s Aquila device via Amazon Braket. Experiments have been conducted with up to 231 qubits on the Aquila quantum device, which is an analog neutral‑atom machine based on Rydberg atoms. The tools are designed specifically for such analog neutral‑atom quantum machines. This article was generated with AI assistance and reviewed by an editor.