An optimized matrix product state representation with DMRG-inspired solver solves the Peierls-Boltzmann transport equation for crystalline silicon phonons with high fidelity at 10^{-3} compression and sublinear scaling in grid size.
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Matrix-product-state calculations on a correlated two-band model show a doping-induced in-gap branch tied to excitonic correlations.
Physics-informed quantum neural networks trained on noisy measurements can construct nontrivial decision boundaries to classify quantum states via order parameters and are suited for NISQ hardware due to links with Markovian open many-body systems.
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Solving the Peierls-Boltzmann transport equation with matrix product states
An optimized matrix product state representation with DMRG-inspired solver solves the Peierls-Boltzmann transport equation for crystalline silicon phonons with high fidelity at 10^{-3} compression and sublinear scaling in grid size.
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Correlation-driven branch in doped excitonic insulators
Matrix-product-state calculations on a correlated two-band model show a doping-induced in-gap branch tied to excitonic correlations.
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Getting large-scale quantum neural networks ready for quantum hardware
Physics-informed quantum neural networks trained on noisy measurements can construct nontrivial decision boundaries to classify quantum states via order parameters and are suited for NISQ hardware due to links with Markovian open many-body systems.