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Physical Review A

Quantum memristors: Towards scalable quantum neuromorphic architectures with coupling

Sergey Yu Stremoukhov, Форш Павел Анатольевич, Anna Frolova, I. A. Kovalishin, D. M. Rusakov, Marat Shakirov, Ksenia Yu. Khabarova, N. Kolachevsky

Published Feb 12, 2026• 0 citations
Publisher
DOI
10.1103/9fy7-4dkb

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We study the entanglement and memristive properties of three coupled quantum memristors. We consider quantum memristors based on conductance-asymmetric superconducting quantum interference device architectures that are coupled via inductors. The three quantum memristors are arranged in two different geometries: linear and triangular coupling configurations. We obtain a variety of correlation measures, including bipartite entanglement and tripartite negativity. For identical quantum memristors, we find that entanglement and memristivity follow the same behavior for the triangular case and the opposite one in the linear case. Finally, we study the multipartite correlations with the tripartite negativity and entanglement monogamy relations, showing that our system has genuine tripartite entanglement. Our results show that quantum correlations in multipartite memristive systems have a nontrivial role and can be used to design quantum memristor arrays for quantum neural networks and neuromorphic quantum computing architectures.
Photonic quantum memristors provide a measurement-induced route to nonlinear and history-dependent quantum dynamics. Experimental demonstrations have so far focused on isolated devices or simple cascaded devices configurations. Here, we experimentally realize and characterize a network of two coupled photonic quantum memristors with crossed feedback, implemented on a silicon nitride photonic integrated circuit and fed by a roomtemperature single-photon source based on a silicon-vacancy color center SiV -in a nanodiamond. Each memristor consists of an integrated Mach-Zehnder interferometer whose transfer function is adaptively updated by photon detection events on another memristor, thus generating novel non-Markovian input-output dynamics with an enhanced memristive behaviour compared to single devices. In particular, we report inter-memristor input-output hysteresis curves exhibiting larger form factors and displaying self-intersecting loops, respectively revealing marked bistability and topologically non-trivial memory dynamics. Furthermore, numerical simulations show how these features emerge from the interplay between memory depth and relative input phase, for both intra-and inter-memristor input-output relations. Our results establish coupled integrated photonic quantum memristors as scalable nonlinear building blocks and highlight their potential for implementing compact quantum neuromorphic and reservoir computing architectures.
Machine learning has recently developed novel approaches, mimicking the synapses of the human brain to achieve similarly efficient learning strategies. Such a method retains the universality of standard ones, while attempting to circumvent their excessive requirements, which hinder their scalability. In this landscape, quantum (or quantum inspired) algorithms may bring enhancement. However, high-performing neural networks invariably display nonlinear behaviors, which poses a challenge to quantum platforms, given the intrinsically linear evolution of closed systems. We propose a strategy to enhance the nonlinearity achievable in this context, without resorting to entangling gates and report the first neuromorphic architecture based on a photonic quantum memristor. In detail, we show how the memristive feedback loop enhances the nonlinearity and hence the performance of the tested algorithms. We test our model on several tasks, widely recognized to benchmark the performance of (quantum) neuromorphic models. We highlight the essential role of the quantum memristive element and demonstrate the possibility of using it as a building block in more sophisticated networks.