Dissipation-induced Quantum Homogenization for Temporal Information Processing

TL;DR

This paper proposes a disordered quantum homogenizer as a robust platform for quantum reservoir computing, capable of processing time-series data through dissipative quantum dynamics, with potential implementations in NMR and photonic systems.

Contribution

It introduces the disordered quantum homogenizer, proving it meets key conditions for reservoir computing and demonstrating its potential for quantum machine learning applications.

Findings

Satisfies stability and contractivity conditions for reservoir dynamics

Can be implemented in NMR ensemble or photonic systems

Potential to function as a quantum reservoir computer

Abstract

Quantum reservoirs have great potential as they utilize the complex real-time dissipative dynamics of quantum systems for information processing and target time-series generation without precise control or fine-tuning of the Hamiltonian parameters. Nonetheless, their realization is challenging as quantum hardware with appropriate dynamics, robustness to noise, and ability to produce target steady states is required. To that end, we propose the disordered quantum homogenizer as an alternative platform, and prove it satisfies the necessary and sufficient conditions - stability and contractivity - of the reservoir dynamics, necessary for solving machine learning tasks with time-series input data streams. The results indicate that the quantum homogenization protocol, physically implementable as either nuclear magnetic resonance ensemble or a photonic system, can potentially function as a reservoir computer.