We introduce a classical computational method for quantum dynamics that relies on a global-in-time variational principle. Unlike conventional time-stepping approaches, our scheme computes the entire state trajectory over a finite time window by minimizing a loss function that enforces Schrödinger’s equation. The variational state is parametrized with a Galerkin-inspired Ansatz based on a time-dependent linear combination of time-independent neural quantum states. This structure is particularly well-suited for exploring long-time dynamics and enables bounding the error with the exact evolution via the global loss function. We showcase the method by simulating global quantum quenches in the paradigmatic transverse-field Ising model in both 1D and 2D, uncovering signatures of ergodicity breaking and the absence of thermalization in two dimensions. Overall, our method is competitive compared to state-of-the-art time-dependent variational approaches, while unlocking previously inaccessible dynamical regimes of strongly interacting quantum systems.

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• Spin dynamics
• Quantum many-body systems
• Artificial neural networks
• Deep learning
• Machine learning
• Monte Carlo methods
• Variational approach