Minimalistic and Scalable Quantum Reservoir Computing Enhanced with Feedback

TL;DR

This paper presents a minimalistic, scalable quantum reservoir computing framework using few atoms, enhanced with feedback and polynomial regression, demonstrating strong performance in memory and nonlinear tasks.

Contribution

Introduces a minimalistic, scalable quantum reservoir computing system with feedback and polynomial regression, improving efficiency and performance in quantum machine learning tasks.

Findings

Effective memory retention demonstrated on Mackey-Glass task

High accuracy in classifying sine-square waveforms

System is scalable with minimal hardware complexity

Abstract

Quantum Reservoir Computing (QRC) leverages quantum systems to perform complex computational tasks with exceptional efficiency and reduced energy consumption. We introduce a minimalistic QRC framework utilizing as few as five atoms in a single-mode optical cavity, combined with continuous quantum measurement. The system is conveniently scalable, as newly added atoms naturally couple with existing ones via the shared cavity field. To achieve high computational expressivity with a minimal reservoir, we include two critical elements: reservoir feedback and polynomial regression. Reservoir feedback modifies the reservoir's dynamics without altering its internal quantum hardware, while polynomial regression nonlinearly enhances output resolution. We demonstrate significant QRC performance in memory retention and nonlinear data processing through two tasks: predicting chaotic time-series data via the Mackey-Glass task and classifying sine-square waveforms. This framework fulfills QRC's objectives to minimize hardware size and energy consumption, marking a significant advancement in integrating quantum physics with machine learning technology.