Quantum Stream Learning

Abstract

The exotic nature of quantum mechanics makes machine learning (ML) be different in the quantum realm compared to classical applications. ML can be used for knowledge discovery using information continuously extracted from a quantum system in a broad range of tasks. The model receives streaming quantum information for learning and decision-making, resulting in instant feedback on the quantum system. As a stream learning approach, we present a deep reinforcement learning on streaming data from a continuously measured qubit at the presence of detuning, dephasing, and relaxation. We also investigate how the agent adapts to another quantum noise pattern by transfer learning. Stream learning provides a better understanding of closed-loop quantum control, which may pave the way for advanced quantum technologies.

Robust optimization for quantum reinforcement learning control using partial observations

“…[38] proposed a robust quantum control protocol against systematic errors by combining Short-cuts-To-Adiabatic (STA) and deep RL methods, which has been verified in the trappedion system [39]. Moreover, weak measurements can be implemented to reduce the measurement cost more significantly [40,41], which have been successfully applied to the double-well and dissipative qubit systems, respectively. Since the state of the qubit is not destroyed but slightly perturbed via weak measurement, these protocols no longer need to record the historical data for repetitive state preparation, and thus may bring significant resource savings in practical implementations.…”