Generalizable control for quantum parameter estimation through reinforcement learning

Abstract

Measurement and estimation of parameters are essential for science and engineering, where one of the main quests is to find systematic and robust schemes that can achieve high precision. While conventional schemes for quantum parameter estimation focus on the optimization of the probe states and measurements, it has been recently realized that control during the evolution can significantly improve the precision. The identification of optimal controls, however, is often computationally demanding, as typically the optimal controls depend on the value of the parameter which then needs to be re-calculated after the update of the estimation in each iteration. Here we show that reinforcement learning provides an efficient way to identify the controls that can be employed to improve the precision. We also demonstrate that reinforcement learning is highly transferable, namely the neural network trained under one particular value of the parameter can work for different values within a broad range. These desired features make reinforcement learning more efficient than conventional optimal quantum control methods.

Optimally controlled quantum discrimination and estimation

“…Quantum discrimination and quantum estimation underlie many applications in quantum information science, including quantum hypothesis testing, quantum detection, and quantum sensing. While quantum control has been employed to improve the precision in quantum estimation [47][48][49][50][51][52][53][54][55][56], the use of quantum control in quantum discrimination remains scarce [57][58][59]. This is so despite the fact that one may expect quantum control to help identify fundamental performance bounds of quantum discrimination, similar to those found for quantum computation [6,60] or derive pulse shapes for improved performance with direct relevance to experiments [8,61].…”

Global optimization of quantum dynamics with AlphaZero deep exploration

“…A shallow depth may broaden exploration, a strategy typically found in Reinforcement Learning (RL) [30]. This has been powerfully combined with Deep Neural Networks (DNN) [31][32][33][34][35] and applied recently to quantum systems [36][37][38][39][40][41][42][43]. Unfortunately, single-step lookaheads are inherently local and thus require a slower learning rate, with no performance gain found over full-depth, domain-specialized (Hessian approximation) methods in QOCT.…”

Improving the dynamics of quantum sensors with reinforcement learning

“…Also in the regime of local parameter estimation, where the parameter is already known to high precision (typically from previous measurements), actor-critic and proximal-policy-optimization RL algorithms were used to find policies to control the dynamics of quantum sensors [30][31][32]. There, the estimation of the precession frequency of a dissipative spin-1 2 particle was improved by adding a linear control to the dynamics in form of an additional controlled magnetic field [32].…”