Scalable Memory Sharing in Photonic Quantum Memristors for Reservoir Computing

• URL: http://arxiv.org/abs/2601.23044v1
• Date: Fri, 30 Jan 2026 14:51:42 GMT
• Title: Scalable Memory Sharing in Photonic Quantum Memristors for Reservoir Computing
• Authors: Chaehyeon Lim, Hyungchul Park, Beomjoon Chae, Jeonghun Kwak, Soo-Yeon Lee, Namkyoo Park, Sunkyu Yu,
• Abstract summary: photonic quantum memristors (PQMRs) have enabled non-Markovian responses but memory remains confined to local elements.<n>Here, we propose a scalable PQMR network that enables measurement-based memory sharing.<n>Our approach paves the way toward high-capacity quantum machine learning using memristive devices compatible with linear-optical quantum computing.
• Score: 3.757436782267772
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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