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**Neural Network Quantum States** (**NQS** or **NNQS**) is a general class of variational quantum states parameterized in terms of an artificial neural network. It was first introduced in 2017 by the physicists Giuseppe Carleo and Matthias Troyer^{[1]} to approximate wave functions of many-body quantum systems.

Given a many-body quantum state comprising degrees of freedom and a choice of associated quantum numbers , then an NQS parameterizes the wave-function amplitudes

where is an artificial neural network of parameters (weights) , input variables () and one complex-valued output corresponding to the wave-function amplitude.

This variational form is used in conjunction with specific stochastic learning approaches to approximate quantum states of interest.

One common application of NQS is to find an approximate representation of the ground state wave function of a given Hamiltonian . The learning procedure in this case consists in finding the best neural-network weights that minimize the variational energy

Since, for a general artificial neural network, computing the expectation value is an exponentially costly operation in , stochastic techniques based, for example, on the Monte Carlo method are used to estimate , analogously to what is done in Variational Monte Carlo, see for example ^{[2]} for a review. More specifically, a set of samples , with , is generated such that they are uniformly distributed according to the Born probability density . Then it can be shown that the sample mean of the so-called "local energy" is a statistical estimate of the quantum expectation value , i.e.

Similarly, it can be shown that the gradient of the energy with respect to the network weights is also approximated by a sample mean

where and can be efficiently computed, in deep networks through backpropagation.

The stochastic approximation of the gradients is then used to minimize the energy typically using a stochastic gradient descent approach. When the neural-network parameters are updated at each step of the learning procedure, a new set of samples is generated, in an iterative procedure similar to what done in unsupervised learning.

Neural-Network representations of quantum wave functions share some similarities with variational quantum states based on tensor networks. For example, connections with matrix product states have been established.^{[3]} These studies have shown that NQS support volume law scaling for the entropy of entanglement. In general, given a NQS with fully-connected weights, it corresponds, in the worse case, to a matrix product state of exponentially large bond dimension in .

**^**Carleo, Giuseppe; Troyer, Matthias (2017). "Solving the quantum many-body problem with artificial neural networks".*Science*.**355**(6325): 602–606. arXiv:1606.02318. Bibcode:2017Sci...355..602C. doi:10.1126/science.aag2302. PMID 28183973. S2CID 206651104.**^**Becca, Federico; Sorella, Sandro (2017).*Quantum Monte Carlo Approaches for Correlated Systems*. Cambridge University Press. doi:10.1017/9781316417041. ISBN 9781316417041.**^**Chen, Jing; Cheng, Song; Xie, Haidong; Wang, Lei; Xiang, Tao (2018). "Equivalence of restricted Boltzmann machines and tensor network states".*Phys. Rev. B*.**97**(8): 085104. arXiv:1701.04831. Bibcode:2018PhRvB..97h5104C. doi:10.1103/PhysRevB.97.085104. S2CID 73659611.