Multiqubit and multilevel quantum reinforcement learning with quantum technologies

TL;DR

This paper introduces a versatile quantum reinforcement learning protocol that does not require coherent feedback, applicable to various quantum systems like trapped ions and superconducting circuits, enhancing quantum control and machine learning.

Contribution

It presents a new quantum reinforcement learning protocol adaptable to multiple quantum platforms without needing coherent feedback during learning.

Findings

Protocol applicable to multiqubit and multilevel systems

Potential implementations in trapped ions and superconducting circuits

Enables improved quantum control and machine learning efficiency

Abstract

We propose a protocol to perform quantum reinforcement learning with quantum technologies. At variance with recent results on quantum reinforcement learning with superconducting circuits, in our current protocol coherent feedback during the learning process is not required, enabling its implementation in a wide variety of quantum systems. We consider diverse possible scenarios for an agent, an environment, and a register that connects them, involving multiqubit and multilevel systems, as well as open-system dynamics. We finally propose possible implementations of this protocol in trapped ions and superconducting circuits. The field of quantum reinforcement learning with quantum technologies will enable enhanced quantum control, as well as more efficient machine learning calculations.