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The complexity of many-body quantum wave functions is a central aspect of several fields of physics and chemistry where non-perturbative interactions are prominent.
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J. W. T. Keeble and A. Rios, “Machine learning the deuteron,” (2019), arXiv:1911.13092 [nucl-th]
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Bruce R. Barrett, Petr Navratil, and James P. Vary, “Ab initio no core shell model,” Prog. Part. Nucl. Phys. 69
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Nobuyuki Yoshioka and Ryusuke Hamazaki, “Constructing neural stationary states for open quantum many-body systems,” Physical Review B 99
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Michael J. Hartmann and Giuseppe Carleo, “Neural-Network Approach to Dissipative Quantum Many-Body Dynamics,” Physical Review Letters 122
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Francesco Ferrari, Federico Becca, and Juan Carrasquilla, “Neural Gutzwiller-projected variational wave functions,” Physical Review B 100
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Yusuke Nomura, “Machine Learning Quantum States — Extensions to Fermion–Boson Coupled Systems and Excited-State Calculations,” Journal of the Physical Society of Japan 89
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Kenny Choo, Antonio Mezzacapo, and Giuseppe Carleo, “Fermionic neural-network states for ab-initio electronic structure,” Nature Communications 11
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