Machine-learning assisted quantum control in random environment

TL;DR

This paper demonstrates that neural networks can effectively learn to control quantum particles in random environments, improving control fidelity and efficiency over traditional methods.

Contribution

Introduces a neural network-based approach for high-fidelity quantum control in disordered systems, showcasing improved efficiency and adaptability.

Findings

Neural networks can recognize disorder patterns and produce control policies.

Higher-dimensional disorder mapping improves control accuracy.

The method outperforms gradient-based optimization in computational efficiency.

Abstract

Disorder in condensed matter and atomic physics is responsible for a great variety of fascinating quantum phenomena, which are still challenging for understanding, not to mention the relevant dynamical control. Here we introduce proof of the concept and analyze neural network-based machine learning algorithm for achieving feasible high-fidelity quantum control of a particle in random environment. To explicitly demonstrate its capabilities, we show that convolutional neural networks are able to solve this problem as they can recognize the disorder and, by supervised learning, further produce the policy for the efficient low-energy cost control of a quantum particle in a time-dependent random potential. We have shown that the accuracy of the proposed algorithm is enhanced by a higher-dimensional mapping of the disorder pattern and using two neural networks, each properly trained for the given task. The designed method, being computationally more efficient than the gradient-descent optimization, can be applicable to identify and control various noisy quantum systems on a heuristic basis.