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Machine learning phases of matter
TLDR
We show 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 from raw state configurations sampled with Monte Carlo. Expand
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 materialsExpand
Quantum Boltzmann Machine
TLDR
We propose a new machine learning approach based on quantum Boltzmann distribution of a transverse-field Ising Hamiltonian. Expand
Long-range order at low temperatures in dipolar spin ice.
It has recently been suggested that long-range magnetic dipolar interactions are responsible for spin ice behavior in the Ising pyrochlore magnets Dy2Ti2O7 and Ho2Ti2O7. We report here numericalExpand
Neural-network quantum state tomography
TLDR
Unsupervised machine learning techniques can efficiently perform quantum state tomography of large, highly entangled states with high accuracy, and allow the reconstruction of many-body quantities from simple experimentally accessible measurements. Expand
Measuring Renyi entanglement entropy in quantum Monte Carlo simulations.
We develop a quantum Monte Carlo procedure, in the valence bond basis, to measure the Renyi entanglement entropy of a many-body ground state as the expectation value of a unitary Swap operator actingExpand
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 iceExpand
Learning Thermodynamics with Boltzmann Machines
TLDR
We develop a Boltzmann machine that is capable of modeling thermodynamic observables for physical systems in thermal equilibrium. Expand
Entanglement at a two-dimensional quantum critical point: a numerical linked-cluster expansion study.
We develop a method to calculate the bipartite entanglement entropy of quantum models, in the thermodynamic limit, using a numerical linked-cluster expansion (NLCE) involving only rectangularExpand
Reconstructing quantum states with generative models
TLDR
A major bottleneck in the development of scalable many-body quantum technologies is the difficulty in benchmarking state preparations, which suffer from an exponential ‘curse of dimensionality’ inherent to the classical description of quantum states. Expand
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