Boosting computational power through spatial multiplexing in quantum reservoir computing

Abstract

Quantum reservoir computing provides a framework for exploiting the natural dynamics of quantum systems as a computational resource. It can implement real-time signal processing and solve temporal machine learning problems in general, which requires memory and nonlinear mapping of the recent input stream using the quantum dynamics in computational supremacy region, where the classical simulation of the system is intractable. A nuclear magnetic resonance spin-ensemble system is one of the realistic candidates for such physical implementations, which is currently available in laboratories. In this paper, considering these realistic experimental constraints for implementing the framework, we introduce a scheme, which we call a spatial multiplexing technique, to effectively boost the computational power of the platform. This technique exploits disjoint dynamics, which originate from multiple different quantum systems driven by common input streams in parallel. Accordingly, unlike designing a single large quantum system to increase the number of qubits for computational nodes, it is possible to prepare a huge number of qubits from multiple but small quantum systems, which are operationally easy to handle in laboratory experiments. We numerically demonstrate the effectiveness of the technique using several benchmark tasks and quantitatively investigate its specifications, range of validity, and limitations in detail.

arXiv comment15 pages

References in corpus (9)

• NMR Techniques for Quantum Control and Computation
• Attractor Reconstruction by Machine Learning
• Harnessing disordered quantum dynamics for machine learning
• Benchmarking quantum control methods on a 12-qubit system
• Quantum Computing with NMR
• Analytical and Numerical Studies of Noise-induced Synchronization of Chaotic Systems
• Experimental observation of a phase transition in the evolution of many-body systems with dipolar interactions
• Macromagnetic simulation for reservoir computing utilizing spin dynamics in magnetic tunnel junctions
• Quantum simulation of spin ordering with nuclear spins in a solid state lattice

Cited by in corpus (60)

• Noisy intermediate-scale quantum (NISQ) algorithms
• Physical reservoir computing -- An introductory perspective
• Opportunities in Quantum Reservoir Computing and Extreme Learning Machines
• Experimental quantum memristor
• Quantum reservoir computing with a single nonlinear oscillator
• Dynamical phase transitions in quantum reservoir computing
• Quantum neuromorphic computing
• Temporal Information Processing on Noisy Quantum Computers
• Gaussian states of continuous-variable quantum systems provide universal and versatile reservoir computing
• Natural quantum reservoir computing for temporal information processing
• Information Processing Capacity of Spin-Based Quantum Reservoir Computing Systems
• Hybrid quantum-classical reservoir computing of thermal convection flow
• Exploring the landscapes of "computing": digital, neuromorphic, unconventional -- and beyond
• A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning
• Potential and limitations of quantum extreme learning machines
• Experimental quantum kernel machine learning with nuclear spins in a solid
• Realising and compressing quantum circuits with quantum reservoir computing
• Complex Quantum Networks: a Topical Review
• Learning Nonlinear Input-Output Maps with Dissipative Quantum Systems
• Dissipation as a resource for Quantum Reservoir Computing
• Learning Temporal Quantum Tomography
• Feedback-driven quantum reservoir computing for time-series analysis
• Periodic structure of memory function in spintronics reservoir with feedback current
• Exploring quantum mechanical advantage for reservoir computing
• On fundamental aspects of quantum extreme learning machines
• Embedding bifurcations into pneumatic artificial muscle
• Step-like dependence of memory function on pulse width in spintronics reservoir computing
• Optimizing quantum noise-induced reservoir computing for nonlinear and chaotic time series prediction
• Reduced-order modeling of two-dimensional turbulent Rayleigh-Bénard flow by hybrid quantum-classical reservoir computing
• Solving the time-complexity problem and tuning the performance of quantum reservoir computing by artificial memory restriction
• Harnessing Quantum Extreme Learning Machines for image classification
• Superpolynomial Quantum Enhancement in Polaritonic Neuromorphic Computing
• Benchmarking the role of particle statistics in Quantum Reservoir Computing
• Optimization of the Memory Reset Rate of a Quantum Echo-State Network for Time Sequential Tasks
• Short-wavelength Reverberant Wave Systems for Physical Realization of Reservoir Computing
• Quantum Extreme Learning of molecular potential energy surfaces and force fields
• Improving the Performance of Echo State Networks Through State Feedback
• Quantum Reservoir Computing for Speckle-Disorder Potentials
• Quantum reservoir computing on random regular graphs
• Quantum Next-Generation Reservoir Computing and Its Quantum Optical Implementation
• Thermally-robust spatiotemporal parallel reservoir computing by frequency filtering in frustrated magnets
• The Roles of Kerr nonlinearity in a Bosonic Quantum Neural Network
• Quantum reservoir probing: an inverse paradigm of quantum reservoir computing for exploring quantum many-body physics
• Simple Hamiltonian dynamics is a powerful quantum processing resource
• Pitfalls when tackling the exponential concentration of parameterized quantum models
• Effects of dissipation in reservoir computing using a spin qubit array
• Predicting three-dimensional chaotic systems with four qubit quantum systems
• Information processing via human soft tissue
• An introduction to reservoir computing
• Enhanced Predictive Capability for Chaotic Dynamics by Modified Quantum Reservoir Computing
• Predicting fermionic densities using a Projected Quantum Kernel method
• Quantum simulations of complex systems
• Engineering Quantum Reservoirs through Krylov Complexity, Expressivity and Observability
• From Krylov Complexity to Observability: Capturing Phase Space Dimension with Applications in Quantum Reservoir Computing
• Quantum Transport Reservoir Computing
• Edge of Many-Body Quantum Chaos in Quantum Reservoir Computing
• Scaling Laws of Quantum Information Lifetime in Monitored Quantum Dynamics
• Feedback Connections in Quantum Reservoir Computing with Mid-Circuit Measurements
• Quantum reservoir computing in Jaynes-Cummings models: Nonlinear memory and time-series prediction
• Data-driven Quantum Dynamical Embedding Method for Long-term Prediction on Near-term Quantum Computers