A new protocol for online amplitude-encoded quantum reservoir computing is proposed that uses mid-circuit measurement and reset to implement partial-trace dynamics and indirect measurements for observables.
Higher-order quantum reservoir computing
4 Pith papers cite this work, alongside 13 external citations. Polarity classification is still indexing.
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In QELMs using XX evolution on image data, classification accuracy saturates comparably to Haar-random unitaries once moderate entanglement develops, remaining classically simulable due to limited entanglement spread.
Indirect measurements in quantum reservoir computing improve execution time scaling, overall performance, and memory capacity over projective measurements and classical feedback methods.
IQFMs iteratively constructs deep quantum feature maps from shallow circuits via classical augmentation weights and contrastive layer-wise training, outperforming QCNNs on noisy quantum data and matching classical neural networks on image classification without variational parameter optimization.
citing papers explorer
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Measurement-enabled online quantum processing with amplitude encoding
A new protocol for online amplitude-encoded quantum reservoir computing is proposed that uses mid-circuit measurement and reset to implement partial-trace dynamics and indirect measurements for observables.
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Entanglement and Classical Simulability in Quantum Extreme Learning Machines
In QELMs using XX evolution on image data, classification accuracy saturates comparably to Haar-random unitaries once moderate entanglement develops, remaining classically simulable due to limited entanglement spread.
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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.
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Iterative Quantum Feature Maps
IQFMs iteratively constructs deep quantum feature maps from shallow circuits via classical augmentation weights and contrastive layer-wise training, outperforming QCNNs on noisy quantum data and matching classical neural networks on image classification without variational parameter optimization.