An iPEPS-based tensor-network approach computes dispersion relations in 2D and 3D quantum systems, with the first such calculations demonstrated for three-dimensional lattices on the transverse-field Ising model.
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Defines influence-solvability for (1+1)D circuits via finite-χ uniform MPS influence matrices, derives local necessary and sufficient conditions from the MPS fundamental theorem, and classifies new solvable brickwork circuits for small local dimensions.
A zero-mode gauge fixing technique truncates bonds in loopy tensor networks by exploiting linear dependencies in the metric tensor of cut-bond states, applied to iPEPS representations of the finite-temperature 2D Z2 lattice gauge theory.
Dynamical correlations in a dissipative XXZ spin chain preserve early-time transport universality classes (ballistic, KPZ, diffusive) with magnon ballistic features at finite magnetization, but acquire exponential damping at long times under Lindblad evolution.
Products of finite-dimensional quantum channels asymptotically forget input states under decay of the centered trace-Dobrushin coefficient, yielding unique replacement channels and convergence for deterministic and random inhomogeneous MPS.
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.
Analytical Pauli-string coefficients plus multistage state refinement let tensor networks find low eigenstates of million-dimensional Laplacians with high fidelity on 20 qubits.
Adapts shadow tomography to tensor networks for O(1) scaling in long-range Hamiltonian expectations and stable variational gradients on classical computers.
COO co-optimizes orbitals with TrimCI to absorb many-body correlations into the basis, cutting determinant count by orders of magnitude for iron-sulfur clusters versus localized bases or DMRG.
The continuum limit of gauged tensor networks is well defined and produces a new class of states for non-perturbative continuum gauge theories.
Spectral bounds relate graph Laplacian eigenvalues to the congestion of binary-tree embeddings, with an efficient spectral-ordering algorithm and applications to tensor-network contraction complexity.
NN-fTNS enhance fermionic tensor networks with neural parametrization to improve expressivity and achieve order-of-magnitude better energies than pure fTNS on Hubbard models while maintaining linear scaling.
Develops a translationally invariant tensor network algorithm to calculate disorder-averaged quantities in infinite random spin chains without sampling, benchmarked on the random transverse-field Ising model at its infinite-randomness critical point.
A tensor-network encoding of TSP tours with Boltzmann weighting and explicit constraint filters that supplies a marginal formula for optimal tours in the zero-temperature exact limit.
Tensor Train compression algorithms detect anomalies by maintaining normal data structure and deleting anomalous structure, tested on digits, faces, and cyber-attack datasets.
Introduces two algorithms for efficient finite initialization of tensor network layers via iterative partial norm computations, applied to MPS/TT and MPO/TT-M layers with scaling analysis and public code.
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.
PEPSKit.jl is a Julia package that supplies high-level algorithms for ground-state, time-evolution and finite-temperature iPEPS simulations with symmetry support on various lattices.
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.
A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.
citing papers explorer
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Dispersion Relations in Two- and Three-Dimensional Quantum Systems
An iPEPS-based tensor-network approach computes dispersion relations in 2D and 3D quantum systems, with the first such calculations demonstrated for three-dimensional lattices on the transverse-field Ising model.
-
Influence-solvability: a systematic theory of $(1+1)D$ solvability and its application to brickwork circuits
Defines influence-solvability for (1+1)D circuits via finite-χ uniform MPS influence matrices, derives local necessary and sufficient conditions from the MPS fundamental theorem, and classifies new solvable brickwork circuits for small local dimensions.
-
Truncating loopy tensor networks by zero-mode gauge fixing: the $Z_2$ lattice gauge theory at finite temperature
A zero-mode gauge fixing technique truncates bonds in loopy tensor networks by exploiting linear dependencies in the metric tensor of cut-bond states, applied to iPEPS representations of the finite-temperature 2D Z2 lattice gauge theory.
-
Dynamical correlations in a dissipative XXZ spin chain
Dynamical correlations in a dissipative XXZ spin chain preserve early-time transport universality classes (ballistic, KPZ, diffusive) with magnon ballistic features at finite magnetization, but acquire exponential damping at long times under Lindblad evolution.
-
Asymptotic Replacement for Quantum Channel Products with Applications to Inhomogeneous Matrix Product States
Products of finite-dimensional quantum channels asymptotically forget input states under decay of the centered trace-Dobrushin coefficient, yielding unique replacement channels and convergence for deterministic and random inhomogeneous MPS.
-
Optimizing ground state preparation protocols with autoresearch
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.
-
Efficient Pauli-decomposition and multistage state-refinement for tensor network based differential equation solver
Analytical Pauli-string coefficients plus multistage state refinement let tensor networks find low eigenstates of million-dimensional Laplacians with high fidelity on 20 qubits.
-
Shadow tomography for classical tensor network simulations
Adapts shadow tomography to tensor networks for O(1) scaling in long-range Hamiltonian expectations and stable variational gradients on classical computers.
-
Absorbing Many-Body Correlations into Core-Optimized Orbitals
COO co-optimizes orbitals with TrimCI to absorb many-body correlations into the basis, cutting determinant count by orders of magnitude for iron-sulfur clusters versus localized bases or DMRG.
-
Continuum limit of gauged tensor network states
The continuum limit of gauged tensor networks is well defined and produces a new class of states for non-perturbative continuum gauge theories.
-
Congestion bounds via Laplacian eigenvalues and their application to tensor networks with arbitrary geometry
Spectral bounds relate graph Laplacian eigenvalues to the congestion of binary-tree embeddings, with an efficient spectral-ordering algorithm and applications to tensor-network contraction complexity.
-
Neuralized Fermionic Tensor Networks for Quantum Many-Body Systems
NN-fTNS enhance fermionic tensor networks with neural parametrization to improve expressivity and achieve order-of-magnitude better energies than pure fTNS on Hubbard models while maintaining linear scaling.
-
Extracting average properties of disordered spin chains with translationally invariant tensor networks
Develops a translationally invariant tensor network algorithm to calculate disorder-averaged quantities in infinite random spin chains without sampling, benchmarked on the random transverse-field Ising model at its infinite-randomness critical point.
-
Tensor-Network Formulation of the Traveling Salesman Problem and Variants
A tensor-network encoding of TSP tours with Boltzmann weighting and explicit constraint filters that supplies a marginal formula for optimal tours in the zero-temperature exact limit.
-
Anomaly Detection from a Tensor Train Perspective
Tensor Train compression algorithms detect anomalies by maintaining normal data structure and deleting anomalous structure, tested on digits, faces, and cyber-attack datasets.
-
Efficient Finite Initialization with Partial Norms for Tensorized Neural Networks and Tensor Networks Algorithms
Introduces two algorithms for efficient finite initialization of tensor network layers via iterative partial norm computations, applied to MPS/TT and MPO/TT-M layers with scaling analysis and public code.
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Quantum-Inspired Hamiltonian Optimization, Stochastic Tensor Networks and Adaptive Congestion Routing for Large-Scale QKD Networks
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.
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PEPSKit.jl: A Julia package for projected entangled-pair state simulations
PEPSKit.jl is a Julia package that supplies high-level algorithms for ground-state, time-evolution and finite-temperature iPEPS simulations with symmetry support on various lattices.
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Quantum-inspired tensor networks in machine learning models
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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A review of quantum machine learning and quantum-inspired applied methods to computational fluid dynamics
A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.