Quantum reservoir processing

Abstract

The concurrent rise of artificial intelligence and quantum information poses opportunity for creating interdisciplinary technologies like quantum neural networks. Quantum reservoir processing, introduced here, is a platform for quantum information processing developed on the principle of reservoir computing that is a form of artificial neural network. A quantum reservoir processor can perform qualitative tasks like recognizing quantum states that are entangled as well as quantitative tasks like estimating a non-linear function of an input quantum state (e.g. entropy, purity or logarithmic negativity). In this way experimental schemes that require measurements of multiple observables can be simplified to measurement of one observable on a trained quantum reservoir processor.

8 pages, 7 figures

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Cited by in corpus (72)

• Noisy intermediate-scale quantum (NISQ) algorithms
• Physical reservoir computing -- An introductory perspective
• Large-Scale Optical Reservoir Computing for Spatiotemporal Chaotic Systems Prediction
• Opportunities in Quantum Reservoir Computing and Extreme Learning Machines
• Experimental quantum memristor
• Realizing Quantum Convolutional Neural Networks on a Superconducting Quantum Processor to Recognize Quantum Phases
• Quantum reservoir computing with a single nonlinear oscillator
• Dynamical phase transitions in quantum reservoir computing
• Quantum neuromorphic computing
• Time Series Quantum Reservoir Computing with Weak and Projective Measurements
• Gaussian states of continuous-variable quantum systems provide universal and versatile reservoir computing
• Information Processing Capacity of Spin-Based Quantum Reservoir Computing Systems
• Learning temporal data with variational quantum recurrent neural network
• Reconstructing quantum states with quantum reservoir networks
• Experimental quantum kernel machine learning with nuclear spins in a solid
• Realising and compressing quantum circuits with quantum reservoir computing
• Resonant Quantum Principal Component Analysis
• Experimental property-reconstruction in a photonic quantum extreme learning machine
• Dissipation as a resource for Quantum Reservoir Computing
• Learning Temporal Quantum Tomography
• The Reservoir Learning Power across Quantum Many-Boby Localization Transition
• Feedback-driven quantum reservoir computing for time-series analysis
• Creating and concentrating quantum resource states in noisy environments using a quantum neural network
• Exploring quantum mechanical advantage for reservoir computing
• On fundamental aspects of quantum extreme learning machines
• Microwave signal processing using an analog quantum reservoir computer
• Classification of four-qubit entangled states via Machine Learning
• Predicting excited states from ground state wavefunction by supervised quantum machine learning
• Optimizing quantum noise-induced reservoir computing for nonlinear and chaotic time series prediction
• Harnessing Quantum Extreme Learning Machines for image classification
• Benchmarking the role of particle statistics in Quantum Reservoir Computing
• Extending echo state property for quantum reservoir computing
• Accurate phonon blockade detector composed of a quadratically coupled optomechanical system
• State estimation with quantum extreme learning machines beyond the scrambling time
• Shaping photons: quantum computation with bosonic cQED
• Witnesses of coherence and dimension from multiphoton indistinguishability tests
• Retrieving past quantum features with deep hybrid classical-quantum reservoir computing
• Tripartite entanglement in quantum memristors
• Squeezing as a resource for time series processing in quantum reservoir computing
• Practical Few-Atom Quantum Reservoir Computing
• Impact of the form of weighted networks on the quantum extreme reservoir computation
• Quantum Next Generation Reservoir Computing: An Efficient Quantum Algorithm for Forecasting Quantum Dynamics
• Photonic kernel machine learning for ultrafast spectral analysis
• Beating the classical phase precision limit using a quantum neuromorphic platform
• Quantum neuromorphic approach to efficient sensing of gravity-induced entanglement
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• Quantum Reservoir Computing for Speckle-Disorder Potentials
• Quantum reservoir computing on random regular graphs
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• The Roles of Kerr nonlinearity in a Bosonic Quantum Neural Network
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• Entanglement estimation of Werner states with a quantum extreme learning machine
• Quantum Convolutional Neural Network for Phase Recognition in Two Dimensions
• Estimating many properties of a quantum state via quantum reservoir processing
• Quantum memristor with vacuum--one-photon qubits
• Quantum Reservoir Computing for Realized Volatility Forecasting
• Predicting three-dimensional chaotic systems with four qubit quantum systems
• Machine learning via relativity-inspired quantum dynamics
• Engineering Quantum Reservoirs through Krylov Complexity, Expressivity and Observability
• Experimental demonstration of enhanced quantum tomography via quantum reservoir processing
• Enhanced Predictive Capability for Chaotic Dynamics by Modified Quantum Reservoir Computing
• Quantum circuit-like learning: A fast and scalable classical machine-learning algorithm with similar performance to quantum circuit learning
• Edge of Many-Body Quantum Chaos in Quantum Reservoir Computing
• Quantum Transport Reservoir Computing
• Physical reservoir computing using finitely-sampled quantum systems
• Quantum reservoir computing for predicting and characterizing chaotic maps
• Polaritonic Machine Learning for Graph-based Data Analysis
• Memory-enhanced quantum extreme learning machines for characterizing non-Markovian dynamics
• Pulsed Quantum Excitation
• The Role of Entanglement in Quantum Reservoir Computing with Coupled Kerr Nonlinear Oscillators