Machine learning engineering of quantum currents

TL;DR

This paper demonstrates how deep reinforcement learning can be used to effectively generate and manipulate quantum current states in circuits, surpassing traditional methods in accuracy and robustness.

Contribution

The authors introduce a novel machine learning approach using deep reinforcement learning to prepare quantum current states in complex circuits, outperforming existing techniques.

Findings

Deep reinforcement learning successfully rediscovered established protocols.

The method can prepare quantum currents with single or multiple winding numbers.

The approach is robust and surpasses traditional methods in accuracy.

Abstract

The design, accurate preparation and manipulation of quantum states in quantum circuits are essential operational tasks at the heart of quantum technologies. Nowadays, circuits can be designed with physical parameters that can be controlled with unprecedented accuracy and flexibility. However, the generation of well-controlled current states is still a nagging bottleneck, especially when different circuit elements are integrated together. In this work, we show how machine learning can effectively address this challenge and outperform the current existing methods. To this end, we exploit deep reinforcement learning to prepare prescribed quantum current states in circuits composed of lumped elements. To highlight our method, we show how to engineer bosonic persistent currents as they are relevant in different quantum technologies as cold atoms and superconducting circuits. We demonstrate the use of deep reinforcement learning to re-discover established protocols, as well as solve configurations that are difficult to treat with other methods. With our approach, quantum current states characterised by a single winding number or entangled currents of multiple winding numbers can be prepared in a robust manner, superseding the existing protocols.