Adaptive quantum dynamics with the time-dependent variational Monte Carlo method
arXiv:2506.08575 · doi:10.1103/qz9c-rrvh
Abstract
We introduce an extension of the time-dependent variational Monte Carlo (tVMC) method that adaptively controls the expressivity of the variational quantum state during the simulation of the dynamics. This adaptive tVMC (atVMC) approach is specifically designed to enhance numerical stability when overparameterized variational ansätze lead to ill-conditioned equations of motion. Building on the concept of the local-in-time error (LITE), a measure of the deviation between variational and exact evolution, we introduce a procedure to quantify each parameter's contribution to reducing the LITE, using only quantities already computed in standard tVMC simulations. These relevance estimates guide the selective evolution of only the most significant parameters at each time step, while maintaining a prescribed level of accuracy. We benchmark the algorithm on quantum quenches in the one-dimensional transverse-field Ising model using both spin-Jastrow and restricted Boltzmann machine wave functions, with an emphasis on overparameterized regimes. The adaptive scheme significantly improves numerical stability and reduces the need for strong regularization, enabling reliable simulations with highly expressive variational ansätze.
12 pages, 5 figures
References in corpus (37)
- Quantum Computing in the NISQ era and beyond
- The density-matrix renormalization group in the age of matrix product states
- Quantum Simulation
- Nonequilibrium dynamics of closed interacting quantum systems
- Probing many-body dynamics on a 51-atom quantum simulator
- Solving the Quantum Many-Body Problem with Artificial Neural Networks
- A Practical Introduction to Tensor Networks: Matrix Product States and Projected Entangled Pair States
- Observation of a Discrete Time Crystal
- Observation of discrete time-crystalline order in a disordered dipolar many-body system
- Time-evolution methods for matrix-product states
- Dynamical quantum phase transitions: a review
- Tensor networks for complex quantum systems
- Theory of variational quantum simulation
- Quantum Entanglement in Neural Network States
- Dynamical Phase Transitions and Instabilities in Open Atomic Many-Body Systems
- Recent progress in many-body localization
- Quantum many-body dynamics in two dimensions with artificial neural networks
- Prethermalization and universal dynamics in near-integrable quantum systems
- Neural-Network Quantum States, String-Bond States, and Chiral Topological States
- Localization and Glassy Dynamics Of Many-Body Quantum Systems
- Light-cone effect and supersonic correlations in one- and two-dimensional bosonic superfluids
- The Tensor Networks Anthology: Simulation techniques for many-body quantum lattice systems
- Quantum phase transition dynamics in the two-dimensional transverse-field Ising model
- Neural tensor contractions and the expressive power of deep neural quantum states
- Quenches near Ising quantum criticality as a challenge for artificial neural networks
- Efficient Tensor Network ansatz for high-dimensional quantum many-body problems
- Unbiasing time-dependent Variational Monte Carlo by projected quantum evolution
- Role of stochastic noise and generalization error in the time propagation of neural-network quantum states
- Local-in-time error in variational quantum dynamics
- Dynamics with autoregressive neural quantum states: application to critical quench dynamics
- Ab-initio variational wave functions for the time-dependent many-electron Schrödinger equation
- Variational classical networks for dynamics in interacting quantum matter
- Highly resolved spectral functions of two-dimensional systems with neural quantum states
- Variational quantum dynamics of two-dimensional rotor models
- Entanglement detection with classical deep neural networks
- Neural Projected Quantum Dynamics: a systematic study
- Efficiency of neural quantum states in light of the quantum geometric tensor