Local tensor-train surrogates approximate quantum machine learning models via Taylor polynomials and tensor networks, delivering polynomial parameter scaling and explicit generalization bounds controlled by patch radius.
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Relativistic continuous matrix product states yield competitive variational approximations to ground state energies and observables in the phi^4, Sine-Gordon, and Sinh-Gordon models, including strongly coupled regimes.
All embedding quantum kernels can be understood as entangled tensor kernels, yielding new insights into their inductive bias and potential dequantization.
Numerical study demonstrates controlled transport of Z4 parafermion edge states in a ladder model and quantifies the adiabatic speed limit under realistic conditions.
Tensor networks developed for quantum states are reviewed as tools for machine learning models, with assessment of their potential computational, explanatory, and privacy advantages alongside remaining challenges.
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Shuttling of $\mathbb{Z}_4$ parafermions in an electronic ladder model
Numerical study demonstrates controlled transport of Z4 parafermion edge states in a ladder model and quantifies the adiabatic speed limit under realistic conditions.
- PEPSKit.jl: A Julia package for projected entangled-pair state simulations