In the large-Hilbert-space limit, Floquet chaotic dynamics with Haar random gates produce linear shot-noise scaling of quantum Fisher information, with super-linear advantages at finite sizes, while local random circuits asymptotically mimic global unitaries.
(Anthony) Chen, A
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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.
Topological entanglement entropy regularizes variational quantum algorithms to enforce quantum sparsity and operate at the edge of chaos for better trainability.
citing papers explorer
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Asymptotic Metrological Scaling and Concentration in Chaotic Floquet Dynamics
In the large-Hilbert-space limit, Floquet chaotic dynamics with Haar random gates produce linear shot-noise scaling of quantum Fisher information, with super-linear advantages at finite sizes, while local random circuits asymptotically mimic global unitaries.
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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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Quantum computation at the edge of chaos
Topological entanglement entropy regularizes variational quantum algorithms to enforce quantum sparsity and operate at the edge of chaos for better trainability.