Experimental quantum memristor

• URL: http://arxiv.org/abs/2105.04867v2
• Date: Mon, 17 May 2021 18:26:07 GMT
• Title: Experimental quantum memristor
• Authors: Michele Spagnolo, Joshua Morris, Simone Piacentini, Michael Antesberger, Francesco Massa, Francesco Ceccarelli, Andrea Crespi, Roberto Osellame and Philip Walther
• Abstract summary: We introduce and experimentally demonstrate a novel quantum-optical memristor based on integrated photonics and acts on single photons. Our device could become a building block of immediate and near-term quantum neuromorphic architectures.
• Score: 0.5396401833457565
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum computer technology harnesses the features of quantum physics for revolutionizing information processing and computing. As such, quantum computers use physical quantum gates that process information unitarily, even though the final computing steps might be measurement-based or non-unitary. The applications of quantum computers cover diverse areas, reaching from well-known quantum algorithms to quantum machine learning and quantum neural networks. The last of these is of particular interest by belonging to the promising field of artificial intelligence. However, quantum neural networks are technologically challenging as the underlying computation requires non-unitary operations for mimicking the behavior of neurons. A landmark development for classical neural networks was the realization of memory-resistors, or "memristors". These are passive circuit elements that keep a memory of their past states in the form of a resistive hysteresis and thus provide access to nonlinear gate operations. The quest for realising a quantum memristor led to a few proposals, all of which face limited technological practicality. Here we introduce and experimentally demonstrate a novel quantum-optical memristor that is based on integrated photonics and acts on single photons. We characterize its memristive behavior and underline the practical potential of our device by numerically simulating instances of quantum reservoir computing, where we predict an advantage in the use of our quantum memristor over classical architectures. Given recent progress in the realization of photonic circuits for neural networks applications, our device could become a building block of immediate and near-term quantum neuromorphic architectures.

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