From Architectures to Applications: A Review of Neural Quantum States
arxiv(2024)
摘要
Due to the exponential growth of the Hilbert space dimension with system
size, the simulation of quantum many-body systems has remained a persistent
challenge until today. Here, we review a relatively new class of variational
states for the simulation of such systems, namely neural quantum states (NQS),
which overcome the exponential scaling by compressing the state in terms of the
network parameters rather than storing all exponentially many coefficients
needed for an exact parameterization of the state. We introduce the commonly
used NQS architectures and their various applications for the simulation of
ground and excited states, finite temperature and open system states as well as
NQS approaches to simulate the dynamics of quantum states. Furthermore, we
discuss NQS in the context of quantum state tomography.
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