Experimental photonic quantum memristor

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. A quantum-optical memristor is realized by means of a laser-written integrated photonic circuit. The memristive dynamics of the device is fully characterized. A memristor-based quantum reservoir computer is proposed as a possible application.

Automated summary

Memristive devices with hysteresis loops in their input-output relations have attracted significant interest in electronics. In this study, researchers propose and experimentally demonstrate a quantum-optical memristor based on integrated photonics that operates on single-photon states. They fully characterize the memristive dynamics of the device and propose a possible application in quantum machine learning using a scheme of quantum reservoir computing.