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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Transformer wave functions for the J1-J2 Heisenberg model exhibit size-independent power-law decay of V-score with compute, with the exponent decreasing as frustration increases.
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.
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.
A new open-source C++ package computes nested commutators [H,[H,...A]] exactly and symbolically in the thermodynamic limit for spin-1/2 hypercubic lattices.
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.
citing papers explorer
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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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Scaling Laws for Neural-Network Quantum States
Transformer wave functions for the J1-J2 Heisenberg model exhibit size-independent power-law decay of V-score with compute, with the exponent decreasing as frustration increases.
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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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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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QCommute: a tool for symbolic computation of nested commutators in quantum many-body spin-1/2 systems
A new open-source C++ package computes nested commutators [H,[H,...A]] exactly and symbolically in the thermodynamic limit for spin-1/2 hypercubic lattices.
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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.
- Recursion method for quench dynamics: strengths and limitations