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    Area of Science:

    • Quantum computing
    • Machine learning
    • Complex systems

    Background:

    • Reservoir computing leverages disordered physical systems for information processing.
    • System observables must be nonlinear functions of input history for effective computation.

    Purpose of the Study:

    • To investigate the computational capabilities of interacting harmonic oscillators in a reservoir computing framework.
    • To assess the performance and noise robustness of oscillator networks in nonlinear tasks.

    Main Methods:

    • Encoding inputs into quantum or classical fluctuations of harmonic oscillator networks.
    • Evaluating performance on nonlinear benchmark tasks using linear Hamiltonians and linear readouts.
    • Analyzing noise robustness by introducing errors in input and reservoir observables.

    Main Results:

    • Oscillator networks achieve high performance comparable to standard echo state networks.
    • Performance remains high even with linear system dynamics and linear readouts.
    • The networks exhibit significant robustness to noise in inputs and observables.

    Conclusions:

    • Interacting harmonic oscillators offer a viable platform for high-performance reservoir computing.
    • Noise robustness is explained by the general principle of weight magnitude in linear readout systems.
    • This work enables reservoir computing using fluctuations in disordered linear systems.