Francesca Schiavello, Edoardo Altamura, Ivano Tavernelli, Stefano Mensa, Benjamin Symons
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Evolving a Multi-Population Evolutionary-QAOA on Distributed QPUs
Our research combines an Evolutionary Algorithm (EA) with a Quantum
Approximate Optimization Algorithm (QAOA) to update the ansatz parameters, in
place of traditional gradient-based methods, and benchmark on the Max-Cut
problem. We demonstrate that our Evolutionary-QAOA (E-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 both
$max\_count$ and Conditional Value at Risk (CVaR) for fitness function
evaluations. Furthermore, we take our algorithm one step further and present a
novel approach by presenting a multi-population EA 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, and we investigated the relative performance accuracy and
variance.