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Optimizing Design Choices for Neural Quantum States
Neural quantum states are a new family of variational ansätze for quantum-many body wave functions with advantageous properties in the …
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Variational Monte Carlo approach to partial differential equations with neural networks
The accurate numerical solution of partial differential equations (PDEs) is a central task in numerical analysis allowing to model a …
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Detecting continuous variable entanglement in phase space with the $Q$-distribution
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General class of continuous variable entanglement criteria
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Optimizing detection of continuous variable entanglement for limited data
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Pair localization in dipolar systems with tunable positional disorder
Strongly interacting quantum systems subject to quenched disorder exhibit intriguing phenomena such as glassiness and many-body …
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Efficient quantum state tomography with convolutional neural networks
Modern day quantum simulators can prepare a wide variety of quantum states but the accurate estimation of observables from tomographic …
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Observation of universal relaxation dynamics in disordered quantum spin systems
A major goal toward understanding far-from-equilibrium dynamics of quantum many-body systems consists in finding indications of …
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Variational Learning of Quantum Ground States on Spiking Neuromorphic Hardware
Summary Recent research has demonstrated the usefulness of neural networks as variational ansatz functions for quantum many-body …
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Absence of thermalization in an interacting system of thousands of quantum spins
Understanding how closed quantum systems dynamically approach thermal equilibrium presents a major unresolved problem in statistical …
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