Information Processing Capacity of Spin-Based Quantum Reservoir Computing Systems

The dynamical behavior of complex quantum systems can be harnessed for information processing. With this aim, quantum reservoir computing (QRC) with Ising spin networks was recently introduced as a quantum version of classical reservoir computing. In turn, reservoir computing is a neuro-inspired mac...

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Bibliographic Details
Published in:Cognitive computation 2023-09, Vol.15 (5), p.1440-1451
Main Authors: Martínez-Peña, R., Nokkala, J., Giorgi, G. L., Zambrini, R., Soriano, M. C.
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computation
Computation by Abstract Devices
Computational Biology/Bioinformatics
Computer Science
Data processing
Dynamical systems
Energy consumption
Information processing
Ising model
Machine learning
Neural networks
Simulation
Time multiplexing
Time series
Trends in Reservoir Computing
Voice recognition
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Summary:The dynamical behavior of complex quantum systems can be harnessed for information processing. With this aim, quantum reservoir computing (QRC) with Ising spin networks was recently introduced as a quantum version of classical reservoir computing. In turn, reservoir computing is a neuro-inspired machine learning technique that consists in exploiting dynamical systems to solve nonlinear and temporal tasks. We characterize the performance of the spin-based QRC model with the Information Processing Capacity (IPC), which allows to quantify the computational capabilities of a dynamical system beyond specific tasks. The influence on the IPC of the input injection frequency, time multiplexing, and different measured observables encompassing local spin measurements as well as correlations is addressed. We find conditions for an optimum input driving and provide different alternatives for the choice of the output variables used for the readout. This work establishes a clear picture of the computational capabilities of a quantum network of spins for reservoir computing. Our results pave the way to future research on QRC both from the theoretical and experimental points of view.
ISSN:1866-9956
1866-9964
DOI:10.1007/s12559-020-09772-y