Recurrent quantum feature maps achieve lower mean squared error than echo state networks and multilayer perceptrons on Mackey-Glass prediction using compact quantum circuits.
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Quantum reservoir computing using a fully connected transverse-field Ising model with input and memory qubits outperforms econometric and standard ML benchmarks in realized volatility forecasting.
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Recurrent Quantum Feature Maps for Reservoir Computing
Recurrent quantum feature maps achieve lower mean squared error than echo state networks and multilayer perceptrons on Mackey-Glass prediction using compact quantum circuits.
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Quantum Reservoir Computing for Realized Volatility Forecasting
Quantum reservoir computing using a fully connected transverse-field Ising model with input and memory qubits outperforms econometric and standard ML benchmarks in realized volatility forecasting.