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.
翻译:我们用一个交互式量子光振动器网络来研究解决连续的非电流优化问题的可能性。 我们建议对与一组量子光学模式的象形操作器相关的模拟信号中的连续变量进行本地编码。 将外部储油层真空波动或对模式的微弱测量所引入的模式和噪音进行光学结合,用于在一组连续随机变量上光学模拟扩散过程。 这一过程持续的时间足够长,足以让它放松到连续域所定义的能源潜力的稳定状态。 作为第一个示范,我们用这些设置来对箱式受限制的二次方程式(BoxQP)问题进行数字基准。 我们考虑实验的延迟线和测量反差变量。 我们的基准结果显示,在这两种情况下,光学网络都能够比一个最先进的古典的超音速三级解决BoxQP问题。</s>