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.
Beth Ruskai, S
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Pauli propagation with tailored truncation enables efficient classical simulation of expectation values for most quantum circuits under any local noise, with high probability and logarithmic effective depth.
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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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Simulating quantum circuits with arbitrary local noise using Pauli Propagation
Pauli propagation with tailored truncation enables efficient classical simulation of expectation values for most quantum circuits under any local noise, with high probability and logarithmic effective depth.