Statistically Characterising Robustness and Fidelity of Quantum Controls and Quantum Control Algorithms

TL;DR

This paper introduces a statistical measure called RIM to quantify the robustness and fidelity of quantum controls, and demonstrates its application in comparing different quantum control algorithms under uncertainty.

Contribution

It proposes the RIM and ARIM metrics for assessing quantum control robustness and fidelity, providing a new framework for algorithm comparison and analysis under uncertainty.

Findings

High fidelity and robustness can coexist in quantum controls.

Reinforcement learning offers cost advantages in noisy optimization scenarios.

RIM effectively characterizes the robustness of various quantum control algorithms.

Abstract

Robustness of quantum operations or controls is important to build reliable quantum devices. The robustness-infidelity measure (RIM$_p$) is introduced to statistically quantify the robustness and fidelity of a controller as the p-order Wasserstein distance between the fidelity distribution of the controller under any uncertainty and an ideal fidelity distribution. The RIM$_p$ is the p-th root of the p-th raw moment of the infidelity distribution. Using a metrization argument, we justify why RIM$_1$ (the average infidelity) suffices as a practical robustness measure. Based on the RIM$_p$, an algorithmic robustness-infidelity measure (ARIM) is developed to quantify the expected robustness and fidelity of controllers found by a control algorithm. The utility of the RIM and ARIM is demonstrated by considering the problem of robust control of spin-$\tfrac{1}{2}$ networks using energy landscape shaping subject to Hamiltonian uncertainty. The robustness and fidelity of individual control solutions as well as the expected robustness and fidelity of controllers found by different popular quantum control algorithms are characterized. For algorithm comparisons, stochastic and non-stochastic optimization objectives are considered, with the goal of effective RIM optimization in the latter. Although high fidelity and robustness are often conflicting objectives, some high fidelity, robust controllers can usually be found, irrespective of the choice of the quantum control algorithm. However, for noisy optimization objectives, adaptive sequential decision making approaches such as reinforcement learning have a cost advantage compared to standard control algorithms and, in contrast, the infidelities obtained are more consistent with higher RIM values for low noise levels.