# Basic protocols in quantum reinforcement learning with superconducting circuits

## TL;DR

This paper proposes implementing basic quantum reinforcement learning protocols using superconducting circuits with feedback control, exploring experimental scenarios and feasibility to advance quantum AI and control.

## Contribution

It introduces novel quantum reinforcement learning protocols with superconducting circuits and analyzes their practical feasibility with current technology.

## Key findings

- Feasibility of quantum reinforcement learning protocols with superconducting circuits.
- Analysis of imperfections affecting protocol implementation.
- Potential for advancing quantum control and quantum AI.

## Abstract

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.