JPMorganChase, Argonne Unveil QAOA Simulation Method for 1 Large-System Limit
Updated
Updated · HPCwire · Sep 3
JPMorganChase, Argonne Unveil QAOA Simulation Method for 1 Large-System Limit
3 articles · Updated · HPCwire · Sep 3
Summary
Researchers from JPMorganChase and Argonne developed a technique that estimates QAOA solution quality on large optimization problems without running the full quantum algorithm end to end.
The method maps the high-depth, infinite-size Sherrington-Kirkpatrick model to a single quantum spin coupled to bosonic modes, replacing costly calculations with simpler matrix product state simulations.
Argonne's Jeffrey Larson also built an optimization approach to find strong QAOA parameters, with simulations run on ALCF and NERSC supercomputers through a DOE INCITE grant.
Published in Physical Review Letters, the work aims to clarify how far quantum optimization algorithms can scale as hardware matures and whether claimed quantum advantage is achievable on larger systems.