A Pauli-transfer-matrix analysis of QELMs reveals the full set of nonlinear Pauli features generated by encoding and transformed by quantum channels, producing an interpretable classical nonlinear vector autoregression model that approximates flow maps in dynamical systems.
Jaeger, Short term memory in echo state networks (2001)
2 Pith papers cite this work. Polarity classification is still indexing.
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Reservoir performance is optimized by modifying minimal representative cycles of one-dimensional GLMY homology groups, with results showing joint influence from network structure and data periodicity.
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Theory and interpretability of Quantum Extreme Learning Machines: a Pauli-transfer matrix approach
A Pauli-transfer-matrix analysis of QELMs reveals the full set of nonlinear Pauli features generated by encoding and transformed by quantum channels, producing an interpretable classical nonlinear vector autoregression model that approximates flow maps in dynamical systems.
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Topology Structure Optimization of Reservoirs Using GLMY Homology
Reservoir performance is optimized by modifying minimal representative cycles of one-dimensional GLMY homology groups, with results showing joint influence from network structure and data periodicity.