Quantum Transport Reservoir Computing

Abstract

Reservoir computing (RC), a neural network designed for temporal data, enables efficient computation with low-cost training and direct physical implementation. Recently, quantum RC has opened new possibilities for conventional RC and introduced novel ideas to tackle open problems in quantum physics and advance quantum technologies. Despite its promise, it faces challenges, including physical realization, output readout, and measurement-induced back-action. Here, we propose to implement quantum RC through quantum transport in mesoscopic electronic systems. Our approach possesses several advantages: compatibility with existing device fabrication techniques, ease of output measurement, and robustness against measurement back-action. Leveraging universal conductance fluctuations, we numerically demonstrate two benchmark tasks, spoken-digit recognition and time-series forecasting, to validate our proposal. This work establishes a novel pathway for implementing on-chip quantum RC via quantum transport and expands the mesoscopic physics applications.

10 pages, 8 figures

References in corpus (37)

• Recent Advances in Physical Reservoir Computing: A Review
• Photonics for artificial intelligence and neuromorphic computing
• Bipolar supercurrent in graphene
• Strong suppression of weak (anti)localization in graphene
• Vowel recognition with four coupled spin-torque nano-oscillators
• Harnessing disordered quantum dynamics for machine learning
• A magnetic skyrmion as a non-linear resistive element - a potential building block for reservoir computing
• High performance photonic reservoir computer based on a coherently driven passive cavity
• Large-Scale Optical Reservoir Computing for Spatiotemporal Chaotic Systems Prediction
• Quantum reservoir processing
• Opportunities in Quantum Reservoir Computing and Extreme Learning Machines
• Reservoir Computing with Random Skyrmion Textures
• Anomalously large conductance fluctuations in weakly disordered graphene
• Boosting computational power through spatial multiplexing in quantum reservoir computing
• Quantum reservoir computing with a single nonlinear oscillator
• Temporal Information Processing on Noisy Quantum Computers
• Gaussian states of continuous-variable quantum systems provide universal and versatile reservoir computing
• Two-dimensional universal conductance fluctuations and the electron-phonon interaction of topological surface states in Bi2Te2Se nanoribbons
• Mesoscopic conductance fluctuations in graphene samples
• Implementing a magnonic time-delay reservoir computer model
• Information Processing Capacity of Spin-Based Quantum Reservoir Computing Systems
• Inelastic Scattering Time for Conductance Fluctuations
• Scalable photonic platform for real-time quantum reservoir computing
• Reservoir Computing with Spin Waves in Skyrmion Crystal
• Reservoir Computing Approach to Quantum State Measurement
• Quantum reservoir computation utilising scale-free networks
• Periodic structure of memory function in spintronics reservoir with feedback current
• Microwave signal processing using an analog quantum reservoir computer
• Experimental Proof of Universal Conductance Fluctuation in Quasi-1D Epitaxial Bi${}_2$Se${}_3$ Wires
• Statistical model of dephasing in mesoscopic devices introduced in the scattering matrix formalism
• Temporal universal conductance fluctuations in RuO${}_2$ nanowires due to mobile defects
• Numerical Study of Universal Conductance Fluctuation in Three-dimensional Topological Semimetals
• Spontaneous breaking of time reversal symmetry in strongly interacting two dimensional electron layers in silicon and germanium
• Electronic coherence in metals: comparing weak localization and time-dependent conductance fluctuations
• Extending echo state property for quantum reservoir computing
• Time-dependent universal conductance fluctuations and coherence in AuPd and Ag
• Universal conductance fluctuations and direct observation of crossover of symmetry classes in topological insulators