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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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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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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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Monte Carlo sampling of complex actions in extended state spaces
Path integrals with complex actions are encountered for many physical systems ranging from spin- or mass-imbalanced atomic gases and …
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Pairing with strings attached
Charge carriers in an engineered bilayer Mott insulator are predicted to form tightly bound, mobile pairs, glued together by string …
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Semiclassical simulations predict glassy dynamics for disordered Heisenberg models
We numerically study out-of-equilibrium dynamics in a family of Heisenberg models with $\frac{1}{r^6}$ power-law interactions and …
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Detecting Entanglement Structure in Continuous Many-Body Quantum Systems
A prerequisite for the comprehensive understanding of many-body quantum systems is a characterization in terms of their entanglement …
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Entropic entanglement criteria in phase space
We derive entropic inseparability criteria for the phase-space representation of quantum states. In contrast to criteria involving …
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Spiking neuromorphic chip learns entangled quantum states
The approximation of quantum states with artificial neural networks has gained a lot of attention during the last years. Meanwhile, …
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