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Two-dimensional frustrated J1−J2 model studied with neural network quantum states
This paper uses a fully convolutional neural network model as a variational ansatz to study the frustrated spin-1/2 J1-J2 Heisenberg model on the square lattice and demonstrates that the resulting predictions for both ground-state energies and properties are competitive with, and often improve upon, existing state-of-the-art methods.
Fermionic neural-network states for ab-initio electronic structure
An extension of neural-network quantum states to model interacting fermionic problems and use neural-networks to perform electronic structure calculations on model diatomic molecules to achieve chemical accuracy.
NetKet: A machine learning toolkit for many-body quantum systems
Symmetries and Many-Body Excitations with Neural-Network Quantum States.
Interestingly, it is found that deep networks typically outperform shallow architectures for high-energy states, and an algorithm to compute low-lying excited states without symmetries is given.
Emergent black hole dynamics in critical Floquet systems
While driven interacting quantum matter is generically subject to heating and scrambling, certain classes of systems evade this paradigm. We study such an exceptional class in periodically driven
Topological many-body scar states in dimensions one, two, and three
We propose an exact construction for atypical excited states of a class of non-integrable quantum many-body Hamiltonians in one dimension (1D), two dimensions (2D), and three dimensins (3D) that
Transport and Energetic Properties of a Ring of Interacting Spins Coupled to Heat Baths
It is shown how the ergotropy of the nonequilibrium steady state can increase significantly near the avoided crossings, and three regimes in which the heat current flows clockwise, counterclockwise, and in parallel are recognized.
Fine structure of heating in a quasiperiodically driven critical quantum system
We study the heating dynamics of a generic one dimensional critical system when driven quasiperiodically. Specifically, we consider a Fibonacci drive sequence comprising the Hamiltonian of uniform
Neural network based classification of crystal symmetries from x-ray diffraction patterns
A scheme where the network has the option to refuse the classification of XRD patterns that would be classified with a large uncertainty is introduced, which enhances the accuracy on experimental data to 82% at the expense of having half of the experimental data unclassified.
Measurement of the Entanglement Spectrum of a Symmetry-Protected Topological State Using the IBM Quantum Computer.
This work uses the IBM quantum computer to make the first ever measurement of the entanglement spectrum of a symmetry-protected topological state, and is able to distinguish its entangler spectrum from those the authors measure for trivial and long-range ordered states.