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Argonne National Laboratory and JPMorganChase have developed a novel simulation technique that maps high-depth Quantum Approximate Optimization Algorithm (QAOA) states to a spin-boson system, enabling the study of large-scale optimization problems without executing them directly on quantum hardware. By leveraging DOE supercomputers to simulate these infinite-size limits, the team establishes a critical baseline for evaluating QAOA performance and identifying its fundamental scaling limits before full quantum advantage is realized. This methodological breakthrough allows researchers to benchmark quantum algorithms against known classical solutions more efficiently than previous end-to-end simulations permitted.
Argonne and JPMorganChase Develop New Method to Study QAOA at Scale

Insider Brief PRESS RELEASE — Quantum computers have arisen as a possible solution for highly complex mathematical problems, offering “quantum advantage” over classical computers in certain cases. The Quantum Approximate Optimization Algorithm (QAOA) is a leading candidate for realizing this advantage, and some success has been achieved for small problems. But demonstrations on large problems have remained […]
Researchers from JPMorganChase and Argonne National Laboratory developed a method to evaluate high-depth QAOA calculations for large Sherrington–Kirkpatrick model problems without running the algorithm end to end. The approach maps the QAOA state in the infinite-size limit to a spin-boson system, allowing researchers to use matrix product state simulations instead of more costly…
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