# Noise-Resilient Quantum Reinforcement Learning

## TL;DR

This paper introduces a noise-resilient quantum reinforcement learning scheme that leverages bound states to counteract decoherence, enhancing performance in noisy quantum environments.

## Contribution

It proposes a universal physical mechanism using bound states to suppress decoherence in quantum reinforcement learning, aiding NISQ algorithm development.

## Key findings

- Formation of a bound state restores QRL performance under noise.
- The mechanism provides a foundation for designing noise-resistant NISQ algorithms.
- Guidelines for practical implementation of noise-resilient QRL are established.

## Abstract

As a branch of quantum machine learning, quantum reinforcement learning (QRL) aims to solve complex sequential decision-making problems more efficiently and effectively than its classical counterpart by exploiting quantum resources. However, in the noisy intermediate-scale quantum (NISQ) era, its realization is challenged by the ubiquitous noise-induced decoherence. Here, we propose a noise-resilient QRL scheme for a quantum eigensolver with a two-level system as an agent. By investigating the non-Markovian decoherence effect on the QRL for solving the eigenstates of the agent-environment interaction Hamiltonian, we find that the formation of a bound state in the energy spectrum of the total agent-noise system restores the QRL performance to that in the noiseless case. Providing a universal physical mechanism to suppress the decoherence effect on quantum machine learning, our result lays the foundation for designing NISQ algorithms and offers a guideline for their practical implementation.