Engineering quantum current states with machine learning

The design, accurate preparation and manipulation of quantum states are essential operational tasks at the heart of quantum technologies. Nowadays, physical parameters of quantum devices and networks 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 within a short time scale and with a high fidelity. To highlight our method, we show how to engineer bosonic persistent currents in ring circuits as they are key ingredients in different quantum technology devices. With our approach, quantum current states characterized by a single winding number or entangled currents with two winding numbers can be prepared superseding the existing protocols. In addition, we generated quantum states entangling a larger set of different winding numbers. Our deep reinforcement learning scheme provides solutions for known challenges in quantum technology and opens new avenues for the control of quantum devices.