Scalable Memory Sharing in Photonic Quantum Memristors for Reservoir Computing
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911411328778240 |
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| author | Lim, Chaehyeon Park, Hyungchul Chae, Beomjoon Kwak, Jeonghun Lee, Soo-Yeon Park, Namkyoo Yu, Sunkyu |
| author_facet | Lim, Chaehyeon Park, Hyungchul Chae, Beomjoon Kwak, Jeonghun Lee, Soo-Yeon Park, Namkyoo Yu, Sunkyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_23044 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Scalable Memory Sharing in Photonic Quantum Memristors for Reservoir Computing Lim, Chaehyeon Park, Hyungchul Chae, Beomjoon Kwak, Jeonghun Lee, Soo-Yeon Park, Namkyoo Yu, Sunkyu Quantum Physics Optics 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. |
| title | Scalable Memory Sharing in Photonic Quantum Memristors for Reservoir Computing |
| topic | Quantum Physics Optics |
| url | https://arxiv.org/abs/2601.23044 |