Basic protocols in quantum reinforcement learning with superconducting circuits
This work addresses the problem of enabling quantum devices to learn and improve through reinforcement learning, which is incremental as it builds on existing superconducting circuit technologies.
The authors tackled the implementation of basic quantum reinforcement learning protocols using superconducting circuits with feedback-loop control, proposing diverse proof-of-principle scenarios and analyzing their feasibility under imperfections.
Superconducting circuit technologies have recently achieved quantum protocols involving closed feedback loops. Quantum artificial intelligence and quantum machine learning are emerging fields inside quantum technologies which may enable quantum devices to acquire information from the outer world and improve themselves via a learning process. Here we propose the implementation of basic protocols in quantum reinforcement learning, with superconducting circuits employing feedback-loop control. We introduce diverse scenarios for proof-of-principle experiments with state-of-the-art superconducting circuit technologies and analyze their feasibility in presence of imperfections. The field of quantum artificial intelligence implemented with superconducting circuits paves the way for enhanced quantum control and quantum computation protocols.