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DTSTAMP:20250626T233533Z
LOCATION:B302-B305
DTSTART;TZID=America/New_York:20241121T100000
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UID:submissions.supercomputing.org_SC24_sess534_post178@linklings.com
SUMMARY:Evolving a Multi-Population Evolutionary-QAOA on Distributed QPUs
DESCRIPTION:Francesca Schiavello, Edoardo Altamura, and Stefano Mensa (Har
 tree Centre, Science and Technology Facilities Council (STFC), UK)\n\nOur 
 research combines an Evolutionary Algorithm with a Quantum Approximate Opt
 imization Algorithm (QAOA) to update the ansatz parameters, in place of tr
 aditional gradient-based methods, and benchmarks on the Max-Cut problem. W
 e 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. Exp
 eriments were conducted on both simulators and quantum hardware, with inve
 stigations in the relative performance accuracy and variance.\n\nRegistrat
 ion Category: Tech Program Reg Pass, Exhibits Reg Pass\n\nSession Chairs: 
 Ayesha Afzal (Friedrich-Alexander University, Erlangen-Nuremberg; Erlangen
  National High Performance Computing Center); Sally Ellingson (University 
 of Kentucky); and Alan Sussman (University of Maryland)\n\n
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