Machine-learning engineering of quantum currents

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 rediscover established protocols, as well as solving configurations that are difficult to treat with other methods. With our approach, quantum current states characterized by a single winding number or entangled currents of multiple winding numbers can be prepared in a robust manner, superseding the existing protocols.