Hybrid quantum-classical reservoir computing enables nonlinear temporal processing of quantum states and outperforms pure quantum or classical reservoirs in both full-tomography and single-axis measurement regimes.
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A fixed time-encoding, not barren-plateau gradient vanishing, sets a 33-dimensional capacity ceiling (11 accessible Fourier modes per observable) that prevents a four-qubit variational QPINN from fitting Lorenz-63.
Indirect measurements in quantum reservoir computing improve execution time scaling, overall performance, and memory capacity over projective measurements and classical feedback methods.
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Temporal processing of quantum states with hybrid quantum-classical reservoirs
Hybrid quantum-classical reservoir computing enables nonlinear temporal processing of quantum states and outperforms pure quantum or classical reservoirs in both full-tomography and single-axis measurement regimes.
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An architectural capacity ceiling, not a barren plateau: why a fixed-encoding variational quantum circuit cannot fit the Lorenz-63 attractor
A fixed time-encoding, not barren-plateau gradient vanishing, sets a 33-dimensional capacity ceiling (11 accessible Fourier modes per observable) that prevents a four-qubit variational QPINN from fitting Lorenz-63.
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Harnessing quantum back-action for time-series processing
Indirect measurements in quantum reservoir computing improve execution time scaling, overall performance, and memory capacity over projective measurements and classical feedback methods.