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Neural-Network Quantum States: A Systematic Review
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The so-called contemporary AI revolution has reached every corner of the social, human and natural sciences -- physics included. In the context of quantum many-body physics, its intersection with machine learning has configured a high-impact interdisciplinary field of study; with the arise of recent seminal contributions that have derived in a large number of publications. One particular research line of such field of study is the so-called Neural-Network Quantum States, a powerful variational computational methodology for the solution of quantum many-body systems that has proven to compete with well-established, traditional formalisms. Here, a systematic review of literature regarding Neural-Network Quantum States is presented.
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Cited by 2 Pith papers
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Symmetry Constraints Regularize Neural Quantum State Learning
Hard-coding translational, reflection, and bit-flip symmetries into Boltzmann-style neural quantum states cuts parameters from thousands to tens and speeds up training while preserving ground-state accuracy, with new ...
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Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications
A variational Monte Carlo algorithm that trains a restricted Boltzmann machine quantum state by sampling a fitted Ising surrogate with a Trotterized quantum circuit, demonstrated on small spin and molecular systems.
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