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Quantum reservoir processing
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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.
Forward citations
Cited by 3 Pith papers
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Efficient classical training of model-free quantum photonic reservoir
Classical light training of photonic quantum reservoirs enables accurate model-free estimation of single-qubit observables and two-qubit entanglement witnesses on unseen quantum states.
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Evaluating quantum circuits in the reservoir computing paradigm
Brickwall quantum circuits with Haar-random, dual-unitary, and solvable two-qubit gates serve as effective reservoirs for temporal processing tasks, with performance correlated to circuit dynamics and validated on syn...
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Evaluating quantum circuits in the reservoir computing paradigm
Brickwall circuits from Haar-random, dual-unitary, and solvable two-qubit gates are tested as quantum reservoirs, showing effective fading memory and prediction accuracy on synthetic time-series data.
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