PSR-NQS makes recurrent neural quantum states scalable for variational Monte Carlo by using parallel scan recurrence, reaching accurate results on 52x52 two-dimensional lattices.
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Neural quantum states simulate dissipative many-body emission dynamics for approximately 40 atoms in dense 1D and 2D arrays, revealing prominent subradiant behavior at late times.
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.
Neural quantum states with a tailored 3D convolutional architecture simulate quench dynamics up to 1000 qubits and verify the 3D quantum Kibble-Zurek mechanism with RG-derived logarithmic corrections and data collapse.
Deep Boltzmann Quantum States with natural-gradient optimization and annealing-like training match exact or best-known solutions for large infinite-range Ising spin glasses and solve job shop scheduling instances.
The Universal Neural Propagator is a single neural model trained self-supervised to predict time evolution in driven quantum many-body systems across arbitrary protocols and initial states.
An unbiased time-dependent variational Monte Carlo method is introduced via self-normalized importance sampling on a cutoff-deformed Born distribution, with a complementary tensor cross interpolation approach explored.
Neural networks learn the score of the probability density on Bohmian trajectories to recover exact Schrödinger dynamics via self-consistent minimization for nodeless wave functions, demonstrated on double-well splitting and Morse chain vibrations.
A learnable Gaussian basis transformation lowers variational energies in neural-network variational Monte Carlo for the three-dimensional homogeneous electron gas.
Charge-pumping simulation extracts Chern numbers and identifies anomalous composite Fermi liquids from neural network wavefunctions in fractional Chern insulators.
Simulations find that the inverse energy gap in 2D Edwards-Anderson spin glasses develops a fat-tailed distribution with infinite variance for large N, while the Sherrington-Kirkpatrick model shows a finite-variance gap scaling roughly as N to the power -1/3.
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.
A general-purpose self-attention Fermi neural network finds chiral p_x ± ip_y superconductivity in an attractive Fermi gas via unbiased energy minimization.
GCNN variational states optimized with directed-loop sampling yield a 4-fold degenerate ground state for V ≤ 0.4 in the quantum dimer model, with benchmarks matching ED and QMC up to L=32.
The paper proposes variational decision diagrams (VDDs) for quantum state representation in QML and reports successful training without barren plateaus on transverse-field Ising and Heisenberg Hamiltonians.
Generalized ML force fields reproduce non-collinear magnetic orders on lattices and predict voltage-driven domain-wall motion in itinerant magnets using extensions to nonequilibrium torques.
Presents a Neural Galerkin method that solves quantum dynamics globally via variational minimization of a Schrödinger loss, demonstrated on 1D/2D transverse-field Ising quenches showing non-thermalization in 2D.
Extrapolated ground-state energy density reaches -0.669441857(7) and sublattice magnetization 0.307447(2) for the 2D S=1/2 Heisenberg antiferromagnet, with finite-size corrections matching chiral perturbation theory.
The superposition of product states ansatz achieves high accuracy for ground state search in 1D and 3D tilted Ising models with short- and long-range interactions as well as random networks.
An unsupervised non-fully-connected deep neural network is applied to two-body systems with spin and isospin degrees of freedom and verified on the deuteron bound state.
citing papers explorer
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Parallel Scan Recurrent Neural Quantum States for Scalable Variational Monte Carlo
PSR-NQS makes recurrent neural quantum states scalable for variational Monte Carlo by using parallel scan recurrence, reaching accurate results on 52x52 two-dimensional lattices.
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Neural network modeling of many-body super- and sub-radiant dynamics
Neural quantum states simulate dissipative many-body emission dynamics for approximately 40 atoms in dense 1D and 2D arrays, revealing prominent subradiant behavior at late times.
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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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Real-time Dynamics in 3D for up to 1000 Qubits with Neural Quantum States: Quenches and the Quantum Kibble--Zurek Mechanism
Neural quantum states with a tailored 3D convolutional architecture simulate quench dynamics up to 1000 qubits and verify the 3D quantum Kibble-Zurek mechanism with RG-derived logarithmic corrections and data collapse.
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Solving Classical and Quantum Spin Glasses with Deep Boltzmann Quantum States
Deep Boltzmann Quantum States with natural-gradient optimization and annealing-like training match exact or best-known solutions for large infinite-range Ising spin glasses and solve job shop scheduling instances.
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Universal Neural Propagator: Learning Time Evolution in Many-Body Quantum Systems
The Universal Neural Propagator is a single neural model trained self-supervised to predict time evolution in driven quantum many-body systems across arbitrary protocols and initial states.
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Time-dependent variational Monte Carlo without bias
An unbiased time-dependent variational Monte Carlo method is introduced via self-normalized importance sampling on a cutoff-deformed Born distribution, with a complementary tensor cross interpolation approach explored.
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Quantum Dynamics via Score Matching on Bohmian Trajectories
Neural networks learn the score of the probability density on Bohmian trajectories to recover exact Schrödinger dynamics via self-consistent minimization for nodeless wave functions, demonstrated on double-well splitting and Morse chain vibrations.
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Enhancing Neural-Network Variational Monte Carlo through Basis Transformation
A learnable Gaussian basis transformation lowers variational energies in neural-network variational Monte Carlo for the three-dimensional homogeneous electron gas.
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Topological invariant of periodic many body wavefunction from charge pumping simulation
Charge-pumping simulation extracts Chern numbers and identifies anomalous composite Fermi liquids from neural network wavefunctions in fractional Chern insulators.
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Energy gap of quantum spin glasses: a projection quantum Monte Carlo study
Simulations find that the inverse energy gap in 2D Edwards-Anderson spin glasses develops a fat-tailed distribution with infinite variance for large N, while the Sherrington-Kirkpatrick model shows a finite-variance gap scaling roughly as N to the power -1/3.
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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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Attention is all you need to solve chiral superconductivity
A general-purpose self-attention Fermi neural network finds chiral p_x ± ip_y superconductivity in an attractive Fermi gas via unbiased energy minimization.
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Group Convolutional Neural Network for the Low-Energy Spectrum in the Quantum Dimer Model
GCNN variational states optimized with directed-loop sampling yield a 4-fold degenerate ground state for V ≤ 0.4 in the quantum dimer model, with benchmarks matching ED and QMC up to L=32.
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Variational decision diagrams for quantum-inspired machine learning applications
The paper proposes variational decision diagrams (VDDs) for quantum state representation in QML and reports successful training without barren plateaus on transverse-field Ising and Heisenberg Hamiltonians.
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Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems
Generalized ML force fields reproduce non-collinear magnetic orders on lattices and predict voltage-driven domain-wall motion in itinerant magnets using extensions to nonequilibrium torques.
-
Time-dependent Neural Galerkin Method for Quantum Dynamics
Presents a Neural Galerkin method that solves quantum dynamics globally via variational minimization of a Schrödinger loss, demonstrated on 1D/2D transverse-field Ising quenches showing non-thermalization in 2D.
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High-precision ground state parameters of the two-dimensional spin-1/2 Heisenberg model on the square lattice
Extrapolated ground-state energy density reaches -0.669441857(7) and sublattice magnetization 0.307447(2) for the 2D S=1/2 Heisenberg antiferromagnet, with finite-size corrections matching chiral perturbation theory.
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Exploring the performance of superposition of product states: from 1D to 3D quantum spin systems
The superposition of product states ansatz achieves high accuracy for ground state search in 1D and 3D tilted Ising models with short- and long-range interactions as well as random networks.
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A neural network approach for two-body systems with spin and isospin degrees of freedom
An unsupervised non-fully-connected deep neural network is applied to two-body systems with spin and isospin degrees of freedom and verified on the deuteron bound state.