From Architectures to Applications: A Review of Neural Quantum States

Hannah Lange, Anka Van de Walle, Atiye Abedinnia, Annabelle Bohrdt
Condensed Matter, Disordered Systems and Neural Networks, Disordered Systems and Neural Networks (cond-mat.dis-nn), Quantum Gases (cond-mat.quant-gas), Strongly Correlated Electrons (cond-mat.str-el), Quantum Physics (quant-ph)
2024-02-14 00:00:00
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.
PDF: From Architectures to Applications: A Review of Neural Quantum States.pdf
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