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Quantum reservoir computing: a reservoir approach toward quantum machine learning on near-term quantum devices
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Quantum systems have an exponentially large degree of freedom in the number of particles and hence provide a rich dynamics that could not be simulated on conventional computers. Quantum reservoir computing is an approach to use such a complex and rich dynamics on the quantum systems as it is for temporal machine learning. In this chapter, we explain quantum reservoir computing and related approaches, quantum extreme learning machine and quantum circuit learning, starting from a pedagogical introduction to quantum mechanics and machine learning. All these quantum machine learning approaches are experimentally feasible and effective on the state-of-the-art quantum devices.
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Cited by 3 Pith papers
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Predicting three-dimensional chaotic systems with four qubit quantum systems
In simulations, quantum reservoir computing with four-qubit reservoirs forecasts eight 3D chaotic systems, reproducing long-term climate for five of them, after per-system hyperparameter tuning.
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Dynamical learning and quantum memory with non-Hermitian many-body systems
In a non-Hermitian spin reservoir on random graphs, the onset of the first exceptional point coincides with an abrupt jump in memory capacity, yielding a tunable learnability threshold.
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A Novel Hybrid Quantum Reservoir Computing (nHQRC) for Phase Transition Detection in Non-Equilibrium Dynamical Systems
A hybrid quantum reservoir with entropy/QFI-triggered gating is claimed to reduce trajectory decay by 13% versus an SVR baseline, but its classification accuracy is near chance and its own table shows it is worse than...
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