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.
Or´ us, A practical introduction to tensor networks: Matrix product states and projected entangled pair states, Annals of Physics349, 117 (2014)
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SCALE and ACE are new convolutional backflow architectures for Neural Quantum States that deliver O(N^3) scaling with high accuracy and over 40x speedup on Hubbard and t-J models up to 32x32 lattices.
An autoregressive sampler draws Pauli strings sequentially from computable conditionals to enable linear-cost fidelity estimation for random matrix product states, with a grouped commuting extension to lower variance.
TNPA uses tensor-network contractions only in a reliable temperature window to seed population annealing, with an effective-sample-size diagnostic to pick the switch-over temperature.
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.
Structured state preparation in QCQMC improves energy accuracy over pure variational methods across molecular, condensed-matter, nuclear, and graph problems.
A variational framework assisted by matrix product states prepares approximate thermal Gibbs states for 1D lattices up to 30 sites and 2D lattices up to 6x6 using up to 44 qubits, with a demonstration on IBM Heron hardware.
Numerical tensor-network study identifies Néel, Ising, collinear, and incommensurate spiral phases plus their transitions in the J1-J2 XY antiferromagnet on the honeycomb lattice.
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.
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Pareto Frontier of Neural Quantum States: Scalable, Affordable, and Accurate Convolutional Backflow for Strongly Correlated Lattice Fermions
SCALE and ACE are new convolutional backflow architectures for Neural Quantum States that deliver O(N^3) scaling with high accuracy and over 40x speedup on Hubbard and t-J models up to 32x32 lattices.
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Verifying random matrix product states with autoregressive local measurements
An autoregressive sampler draws Pauli strings sequentially from computable conditionals to enable linear-cost fidelity estimation for random matrix product states, with a grouped commuting extension to lower variance.
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Tensor-Network Population Annealing
TNPA uses tensor-network contractions only in a reliable temperature window to seed population annealing, with an effective-sample-size diagnostic to pick the switch-over temperature.
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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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A unified quantum computing quantum Monte Carlo framework through structured state preparation
Structured state preparation in QCQMC improves energy accuracy over pure variational methods across molecular, condensed-matter, nuclear, and graph problems.
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Variational Thermal State Preparation on Digital Quantum Processors Assisted by Matrix Product States
A variational framework assisted by matrix product states prepares approximate thermal Gibbs states for 1D lattices up to 30 sites and 2D lattices up to 6x6 using up to 44 qubits, with a demonstration on IBM Heron hardware.
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Magnetic phases in the $J_{1}$-$J_{2}$ antiferromagnetic XY model on the honeycomb lattice
Numerical tensor-network study identifies Néel, Ising, collinear, and incommensurate spiral phases plus their transitions in the J1-J2 XY antiferromagnet on the honeycomb lattice.