Scalable Memory Sharing in Photonic Quantum Memristors for Reservoir Computing

TL;DR

This paper introduces a scalable photonic quantum memristor network that enables distributed memory sharing, enhancing quantum reservoir computing performance for machine learning tasks like Fashion-MNIST classification.

Contribution

It proposes a novel scalable architecture for measurement-based quantum memristor networks that facilitates shared memory across nodes, improving quantum and classical hysteresis and machine learning accuracy.

Findings

Enhanced classical and quantum hysteresis at device level

Improved Fashion-MNIST classification accuracy

Demonstrated distributed memory sharing in quantum networks

Abstract

Although photons are robust, room-temperature carriers well suited to quantum machine learning, the absence of photon-photon interactions hinder the realization of memory functionalities that are critical for capturing long-range context. Recently, measurement-based implementations of photonic quantum memristors (PQMRs) have enabled tunable non-Markovian responses. However, their memory remains confined to local elements, in contrast to biological or artificial networks where memory is shared across the system. Here, we propose a scalable PQMR network that enables measurement-based memory sharing. Each memristive node updates its internal state using the history of its own and neighbouring quantum states, thereby realizing distributed memory. By modelling each node as a photonic quantum memtransistor, we demonstrate pronounced enhancements in both classical and quantum hysteresis at the device level, as well as enhanced network-level quantum hysteresis. Implemented as a quantum reservoir, the architecture achieves improved Fashion-MNIST classification accuracy and confidence via increased data separability. Our approach paves the way toward high-capacity quantum machine learning using memristive devices compatible with linear-optical quantum computing.