Robust Quantum Control in Games: an Adversarial Learning Approach

Abstract

High-precision operation of quantum computing systems must be robust to uncertainties and noises in the quantum hardware. In this paper, we show that through a game played between the uncertainties (or noises) and the controls, adversarial uncertainty samples can be generated to find highly robust controls through the search for Nash equilibria (NE). We propose a broad family of adversarial learning algorithms, namely a-GRAPE algorithms, which include two effective learning schemes referred to as the best-response approach and the better-response approach within the game-theoretic terminology, providing options for rapidly learning robust controls. Numerical experiments demonstrate that the balance between fidelity and robustness depends on the details of the chosen adversarial learning algorithm, which can effectively lead to a significant enhancement of control robustness while attaining high fidelity.

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• Hessian-based optimization of constrained quantum control
• Robust quantum gates using smooth pulses and physics-informed neural networks
• Robust population inversion in three-level systems by composite pulses
• Robust Control of Quantum Dynamics under Input and Parameter Uncertainty
• Supervised learning for robust quantum control in composite-pulse systems
• Risk-sensitive Optimization for Robust Quantum Controls
• Robust and optimal control of open quantum systems
• Machine Learning for Estimation and Control of Quantum Systems
• Robust optimization for quantum reinforcement learning control using partial observations
• Robust Control Optimization for Quantum Approximate Optimization Algorithm
• Dynamical uncertainty propagation with noisy quantum parameters
• Fault-tolerant $H^{\infty }$ control for optical parametric oscillators with pumping fluctuations
• Analyzing and Unifying Robustness Measures for Excitation Transfer Control in Spin Networks
• Fault-tolerant Coherent H-infinity Control for Linear Quantum Systems