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A fundamental problem in quantum many-body physics is that of finding ground states of local Hamiltonians.
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Pierre L’Ecuyer and Christiane Lemieux · 2002
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Sven Bachmann, Spyridon Michalakis, Bruno Nachtergaele, and Robert Sims · 2012
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Predicting excited states from ground state wavefunction by supervised quantum machine learning
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Deep learning the hohenberg-kohn maps of density functional theory
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Predicting properties of quantum systems with conditional generative models
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Using shadows to learn ground state properties of quantum hamiltonians
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Classical shadows with pauli-invariant unitary ensembles
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Provably efficient learning of phases of matter via dissipative evolutions
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Learning to predict arbitrary quantum processes
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Improved machine learning algorithm for predicting ground state properties
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