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Machine learning phases of matter
TLDR
It is shown that modern machine learning architectures, such as fully connected and convolutional neural networks, can identify phases and phase transitions in a variety of condensed-matter Hamiltonians.
Quantum Boltzmann Machine
TLDR
This work proposes a new machine learning approach based on quantum Boltzmann distribution of a transverse-field Ising Hamiltonian that allows the QBM efficiently by sampling and discusses the possibility of using quantum annealing processors like D-Wave for QBM training and application.
TOPICAL REVIEW: Monte Carlo studies of the dipolar spin ice model
We present a detailed overview of numerical Monte Carlo studies of the dipolar spin ice model, which has been shown to be an excellent quantitative descriptor of the Ising pyrochlore materials
Long-range order at low temperatures in dipolar spin ice.
TLDR
Numerical results on the low temperature properties of the dipolar spin ice model are reported, obtained via a new loop algorithm which greatly improves the dynamics at low temperature.
Measuring Renyi entanglement entropy in quantum Monte Carlo simulations.
TLDR
A quantum Monte Carlo procedure is developed to measure the Renyi entanglement entropy of a many-body ground state as the expectation value of a unitary Swap operator acting on two copies of the system.
Neural-network quantum state tomography
TLDR
It is demonstrated that machine learning allows one to reconstruct traditionally challenging many-body quantities—such as the entanglement entropy—from simple, experimentally accessible measurements, and can benefit existing and future generations of devices.
Spin correlations in Ho2Ti2O7: a dipolar spin ice system.
The pyrochlore material Ho2Ti2O7 has been suggested to show "spin ice" behavior. We present neutron scattering and specific heat results that establish unambiguously that Ho2Ti2O7 exhibits spin ice
Entanglement at a two-dimensional quantum critical point: a numerical linked-cluster expansion study.
TLDR
NLCE is used to obtain universal pieces of the entanglement entropy associated with lines and corners at the quantum critical point and is shown to be one of the few methods capable of accurately calculating universal properties of arbitrary Renyi entropies at higher dimensional critical points.
Recurrent neural network wave functions
TLDR
The effectiveness of RNN wavefunctions is demonstrated by calculating ground state energies, correlation functions, and entanglement entropies for several quantum spin models of interest to condensed matter physicists in one and two spatial dimensions.
Topological entanglement entropy of a Bose-Hubbard spin liquid
Spin liquids are states of matter that reside outside the regime where the Landau paradigm for classifying phases can be applied. This makes them interesting, but also hard to find, as no
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