Experimental photonic quantum memristor

TLDR

Memristive devices exhibit history‑dependent hysteresis and have attracted interest for energy‑efficient memories, neural networks, and neuromorphic computing, yet quantum memristor proposals so far lack practical feasibility. The study proposes and experimentally demonstrates a photonic quantum memristor that operates on single‑photon states. The device’s memristive dynamics were fully characterized and its quantum output state tomographically reconstructed, and it was further applied to quantum reservoir computing for classical and quantum learning tasks. Simulations indicate promising performance, suggesting quantum memristors could advance quantum neuromorphic architectures.

Abstract

Abstract Memristive devices are a class of physical systems with history-dependent dynamics characterized by signature hysteresis loops in their input–output relations. In the past few decades, memristive devices have attracted enormous interest in electronics. This is because memristive dynamics is very pervasive in nanoscale devices, and has potentially groundbreaking applications ranging from energy-efficient memories to physical neural networks and neuromorphic computing platforms. Recently, the concept of a quantum memristor was introduced by a few proposals, all of which face limited technological practicality. Here we propose and experimentally demonstrate a novel quantum-optical memristor (based on integrated photonics) that acts on single-photon states. We fully characterize the memristive dynamics of our device and tomographically reconstruct its quantum output state. Finally, we propose a possible application of our device in the framework of quantum machine learning through a scheme of quantum reservoir computing, which we apply to classical and quantum learning tasks. Our simulations show promising results, and may break new ground towards the use of quantum memristors in quantum neuromorphic architectures.