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arXiv · 2307.14853

Photonic counterdiabatic quantum optimization algorithm

Pranav Chandarana·Koushik Paul·Mikel Garcia-de-Andoin·Yue Ban·Mikel Sanz·Xi Chen
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Abstract

We propose a hybrid quantum-classical approximate optimization algorithm for photonic quantum computing, specifically tailored for addressing continuous-variable optimization problems. Inspired by counterdiabatic protocols, our algorithm significantly reduces the required quantum operations for optimization as compared to adiabatic protocols. This reduction enables us to tackle non-convex continuous optimization and countably infinite integer programming within the near-term era of quantum computing. Through comprehensive benchmarking, we demonstrate that our approach outperforms existing state-of-the-art hybrid adiabatic quantum algorithms in terms of convergence and implementability. Remarkably, our algorithm offers a practical and accessible experimental realization, bypassing the need for high-order operations and overcoming experimental constraints. We conduct proof-of-principle experiments on an eight-mode nanophotonic quantum chip, successfully showcasing the feasibility and potential impact of the algorithm.

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Pranav Chandarana, Koushik Paul, Mikel Garcia-de-Andoin, Yue Ban, Mikel Sanz, Xi Chen. 2023-07-27. Photonic counterdiabatic quantum optimization algorithm. https://doi.org/10.1038/s42005-024-01807-2

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