Argonne and JPMorganChase Develop New Method to Study QAOA at Scale

Insider Brief

• 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 calculations.
• The researchers used supercomputers at DOE computing facilities to simulate the system and optimize QAOA parameters, providing a way to study the performance and limits of quantum optimization algorithms.

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 too computationally costly to run on classical computers and current quantum hardware.

Researchers from JPMorganChase and the U.S. Department of Energy’s (DOE) Argonne National Laboratory have now found a way to make such calculations possible. Their results were published in Physical Review Letters.