Tensor networks enable tunable, objective compression of 1D fluid data with lossless reconstruction at high bond dimension and efficient in-compressed-space operations like periodic convolution.
Density-matrix algorithms for quantum renormalization groups,
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AI coding agents evolve simple ground-state protocols into improved versions for VQE, DMRG, and AFQMC on spin models and molecules by using executable energy scores under fixed compute budgets.
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.
Tensor network scans reveal that the stationary spin entanglement entropy ridge follows population phase boundaries at small s but lacks the two-branch structure at large s in the sub-Ohmic spin-boson model.
Canonical mapping of quantum-dot-superconductor clusters enables neural quantum-state calculations that reveal trivial singlet, Heisenberg-like, and critical regimes with 1D gaplessness and 2D triplet states.
DMFT on the 2D Hubbard-Holstein model produces two Fermi-resonance peaks in electronic friction missed by MFT, with EF-LD simulations revealing substantial differences in electron population dynamics.
Derives static effective Hamiltonians via cRPA and mRPA downfolding with double-counting corrections and compares performance on benzene ground state and bond dissociation curves.
Numerical study demonstrates controlled transport of Z4 parafermion edge states in a ladder model and quantifies the adiabatic speed limit under realistic conditions.
A quantum-inspired framework using effective Hamiltonians, Metropolis annealing and stochastic tensor-network compression is proposed for adaptive multi-demand routing in large-scale QKD networks.
Perspective reviewing TTNS-DMRG methods for computing thousands of vibrational eigenstates in molecules up to 33 dimensions, with emphasis on connections to ML-MCTDH and practical challenges.
Introductory lecture notes on tensor networks with emphasis on matrix-product states, their algorithms, higher-dimensional generalizations, and applications to mixed states and open quantum systems, accompanied by Julia code.
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Some progress on the use of the variational method in quantum field theory
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.