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Evolving a Multi-Population Evolutionary-QAOA on Distributed QPUs
DescriptionOur research combines an Evolutionary Algorithm with a Quantum Approximate Optimization Algorithm (QAOA) to update the ansatz parameters, in place of traditional gradient-based methods, and benchmarks on the Max-Cut problem. We demonstrate that our Evolutionary-QAOA pairing performs on par or better than a COBYLA-based QAOA in terms of solution accuracy and variance, for d-3 regular graphs between 4 and 26 nodes, using Conditional Value at Risk for fitness function evaluations. Furthermore, we take our algorithm one step further and present a novel approach by presenting a multi-population algorithm distributed on two QPUs, which evolves independent and isolated populations in parallel, classically communicating elite individuals. Experiments were conducted on both simulators and quantum hardware, with investigations in the relative performance accuracy and variance.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Doctoral Showcase
Posters
TimeTuesday, 19 November 202412pm - 5pm EST
LocationB302-B305
TP
XO/EX