Non-unital noise in a superconducting quantum computer as a computational resource for reservoir computing

TL;DR

This paper demonstrates that non-unital noise, specifically amplitude damping, can enhance the performance of quantum reservoir computing on superconducting qubits by improving memory and dynamics, with an optimal dissipation rate identified.

Contribution

It introduces a noise model that turns a typical detrimental effect into a computational resource for quantum reservoir computing.

Findings

Optimal dissipation rate around γ≈0.03 improves network performance.

Energy dissipation enhances short-term memory and expressivity.

Beneficial effects of noise are stable even at higher noise intensities.

Abstract

We identify a noise model that ensures the functioning of an echo state network employing a gate-based quantum computer for reservoir computing applications. Energy dissipation induced by amplitude damping drastically improves the short-term memory capacity and expressivity of the network, by simultaneously providing fading memory and richer dynamics. There is an ideal dissipation rate that ensures the best operation of the echo state network around $\gamma\sim$ 0.03. Nevertheless, these beneficial effects are stable as the intensity of the applied noise increases. The improvement of the learning is confirmed by emulating a realistic noise model applied to superconducting qubits, paving the way for the application of reservoir computing methods in current non-fault-tolerant quantum computers.