Non-convex Quadratic Programming Using Coherent Optical Networks

• URL: http://arxiv.org/abs/2209.04415v2
• Date: Fri, 16 Sep 2022 22:56:51 GMT
• Title: Non-convex Quadratic Programming Using Coherent Optical Networks
• Authors: Farhad Khosravi, Ugur Yildiz, Artur Scherer, and Pooya Ronagh
• Abstract summary: We numerically benchmark solving box-constrained quadratic (BoxQP) problems using these settings. In both cases the optical network is capable of solving BoxQP problems three magnitude faster than a state-of-the-art classical classical programming.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We investigate the possibility of solving continuous non-convex optimization problems using a network of interacting quantum optical oscillators. We propose a native encoding of continuous variables in analog signals associated with the quadrature operators of a set of quantum optical modes. Optical coupling of the modes and noise introduced by vacuum fluctuations from external reservoirs or by weak measurements of the modes are used to optically simulate a diffusion process on a set of continuous random variables. The process is run sufficiently long for it to relax into the steady state of an energy potential defined on a continuous domain. As a first demonstration, we numerically benchmark solving box-constrained quadratic programming (BoxQP) problems using these settings. We consider delay-line and measurement-feedback variants of the experiment. Our benchmarking results demonstrate that in both cases the optical network is capable of solving BoxQP problems over three orders of magnitude faster than a state-of-the-art classical heuristic.

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