Sample-efficient adaptive calibration of quantum networks using Bayesian optimization

Abstract

Indistinguishable photons are imperative for advanced quantum communication networks. Indistinguishability is difficult to obtain because of environment-induced photon transformations and loss imparted by communication channels, especially in noisy scenarios. Strategies to mitigate these transformations often require hardware or software overhead that is restrictive (e.g. adding noise), infeasible (e.g. on a satellite), or time-consuming for deployed networks. Here we propose and develop resource-efficient Bayesian optimization techniques to rapidly and adaptively calibrate the indistinguishability of individual photons for quantum networks using only information derived from their measurement. To experimentally validate our approach, we demonstrate the optimization of Hong-Ou-Mandel interference between two photons-a central task in quantum networking-finding rapid, efficient, and reliable convergence towards maximal photon indistinguishability in the presence of high loss and shot noise. We expect our resource-optimized and experimentally friendly methodology will allow fast and reliable calibration of indistinguishable quanta, a necessary task in distributed quantum computing, communications, and sensing, as well as for fundamental investigations.

Active Learning for the Optimal Design of Multinomial Classification in Physics

“…The application of AL is not bounded to quantum information retrieval, where we have shown the trade-off between extracted quantum information (model refining) and fidelity loss (cost of labeling). Recently, it is also employed to assist experimental control [40][41][42], computational physics [43][44][45][46], quantum machine learning [47][48][49], etc., attaining convincing performance as well. Based on these facts, we conclude that most of the physics problems can be efficiently studied by AL, if they can be equivalently represented by classification problems.…”