Experimental quantum memristor

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.

arXiv commentFirst revision

References in corpus (23)

• Deep Learning with Coherent Nanophotonic Circuits
• Recent Advances in Physical Reservoir Computing: A Review
• Memory effects in complex materials and nanoscale systems
• All-optical Reservoir Computing
• An organic nanoparticle transistor behaving as a biological synapse
• Harnessing disordered quantum dynamics for machine learning
• Laser written waveguide photonic quantum circuits
• Neuromorphic, Digital and Quantum Computation with Memory Circuit Elements
• Quantum reservoir processing
• Solving mazes with memristors: a massively-parallel approach
• Quantum-optical state engineering up to the two-photon level
• Opportunities in Quantum Reservoir Computing and Extreme Learning Machines
• Boosting computational power through spatial multiplexing in quantum reservoir computing
• On the physical properties of memristive, memcapacitive, and meminductive systems
• Memristor - The fictional circuit element
• Quantum Memristors
• Quantum Memristors with Superconducting Circuits
• Symmetric polarization insensitive directional couplers fabricated by femtosecond laser waveguide writing
• Low power reconfigurability and reduced crosstalk in integrated photonic circuits fabricated by femtosecond laser micromachining
• Quantum Memristors in Quantum Photonics
• Reconfigurable photonics on a glass chip
• Optical tomography of Fock state superpositions
• Thermal phase shifters for femtosecond laser written photonic integrated circuits

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• Unidirectional scattering with spatial homogeneity using photonic time disorder
• Role of coherence in many-body Quantum Reservoir Computing
• Extending echo state property for quantum reservoir computing
• Characterization of multi-mode linear optical networks
• Benefits of Open Quantum Systems for Quantum Machine Learning
• Quantum Machine Learning Implementations: Proposals and Experiments
• Tripartite entanglement in quantum memristors
• Squeezing as a resource for time series processing in quantum reservoir computing
• Retrieving past quantum features with deep hybrid classical-quantum reservoir computing
• Quantum Memristors with Quantum Computers
• Tunable Non-Markovianity for Bosonic Quantum Memristors
• Quantum reinforcement learning in the presence of thermal dissipation
• A micro-opto-mechanical glass interferometer for megahertz modulation of optical signals
• Quantum Next-Generation Reservoir Computing and Its Quantum Optical Implementation
• Microwave Quantum Memcapacitor Effect
• Demonstration of Hardware Efficient Photonic Variational Quantum Algorithm
• Optimized surface ion trap design for tight confinement and separation of ion chains
• Machine Learning for maximizing the memristivity of single and coupled quantum memristors
• Evaluating the transport properties of interface-type, analog memristors
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• Generating Quantum Reservoir State Representations with Random Matrices
• Enhanced Predictive Capability for Chaotic Dynamics by Modified Quantum Reservoir Computing
• Variational quantum cloning machine on an integrated photonic interferometer
• Neural networks with quantum states of light
• Optical Quantum Computing