Optimal quantum reservoir computing for the NISQ era

TL;DR

This paper introduces a criterion for selecting optimal quantum reservoirs in NISQ devices, demonstrating improved performance with fewer gates and providing insights into the complexity of quantum states.

Contribution

It proposes a simple, gate-efficient criterion for quantum reservoir selection, enhancing performance and understanding in NISQ quantum machine learning.

Findings

Optimal reservoirs outperform common models with fewer gates

Selected reservoirs achieve better computational results

Provides theoretical insights into quantum state complexity

Abstract

Universal fault-tolerant quantum computers require millions of qubits with low error rates. Since this technology is years ahead, noisy intermediate-scale quantum (NISQ) computation is receiving tremendous interest. In this setup, quantum reservoir computing is a relevant machine learning algorithm. Its simplicity of training and implementation allows to perform challenging computations on today available machines. In this Letter, we provide a criterion to select optimal quantum reservoirs, requiring few and simple gates. Our findings demonstrate that they render better results than other commonly used models with significantly less gates, and also provide insight on the theoretical gap between quantum reservoir computing and the theory of quantum states complexity.