Year
Month
(Peer-Reviewed) Coupled integrated photonic quantum memristors using a single photon source made of a colour center
Alessio Baldazzi ¹, Roy Philip George Konnoth Ancel ², Sebastiano Guaraldo ¹, Ivan Fattori ¹, Xuan Chen ¹, Ziad Abi Akar ², Regis Deturche ², Stefano Azzini ¹, Christophe Couteau ², Lorenzo Pavesi ¹
¹ Department of Physics, University of Trento, Via Sommarive 14, Trento, 38123, Italy
² Laboratory Light, nanomaterials and nanotechnologies - L2n UMR 7076, University of Technology of Troyes-UTT and CNRS, 12 Rue Marie Curie, 10300 Troyes, France
Opto-Electronic Advances, 2026-10-25
Abstract

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 room-temperature 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 self-intersecting hysteresis geometry. 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.

We experimentally test the performance of our system in the NARMA task. 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.
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