An Empirical Study of Quantum Dynamics as a Ground State Problem with Neural Quantum States
This work addresses the challenge of simulating quantum dynamics for researchers in quantum physics and machine learning, but it is incremental as it focuses on assessing existing properties in a specific context.
The study tackled the problem of using neural quantum states to approximate the ground state in the Feynman-Kitaev formalism for a spin chain, finding that while the neural networks are expressive enough to accurately approximate the true ground state, trainability becomes increasingly difficult as the number of time steps increases due to higher entanglement and spreading probability distributions.
We consider the Feynman-Kitaev formalism applied to a spin chain described by the transverse field Ising model. This formalism consists of building a Hamiltonian whose ground state encodes the time evolution of the spin chain at discrete time steps. To find this ground state, variational wave functions parameterised by artificial neural networks -- also known as neural quantum states (NQSs) -- are used. Our work focuses on assessing, in the context of the Feynman-Kitaev formalism, two properties of NQSs: expressivity (the possibility that variational parameters can be set to values such that the NQS is faithful to the true ground state of the system) and trainability (the process of reaching said values). We find that the considered NQSs are capable of accurately approximating the true ground state of the system, i.e., they are expressive enough ansätze. However, extensive hyperparameter tuning experiments show that, empirically, reaching the set of values for the variational parameters that correctly describe the ground state becomes ever more difficult as the number of time steps increase because the true ground state becomes more entangled, and the probability distribution starts to spread across the Hilbert space canonical basis.