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Found 16 papers, 7 papers with code
CaloQVAE : Simulating high-energy particle-calorimeter interactions using hybrid quantum-classical generative models
The Large Hadron Collider's high luminosity era presents major computational challenges in the analysis of collision events.
Investigating Topological Order using Recurrent Neural Networks
Recurrent neural networks (RNNs), originally developed for natural language processing, hold great promise for accurately describing strongly correlated quantum many-body systems.
Supplementing Recurrent Neural Network Wave Functions with Symmetry and Annealing to Improve Accuracy
We use symmetry and annealing to obtain accurate estimates of ground state energies of the two-dimensional (2D) Heisenberg model, on the square lattice and on the triangular lattice.
Twin Neural Network Regression is a Semi-Supervised Regression Algorithm
Twin neural network regression (TNNR) is a semi-supervised regression algorithm, it can be trained on unlabelled data points as long as other, labelled anchor data points, are present.
Variational Neural Annealing
Many important challenges in science and technology can be cast as optimization problems.
Twin Neural Network Regression
The solution of a traditional regression problem is then obtained by averaging over an ensemble of all predicted differences between the targets of an unseen data point and all training data points.
Reconstructing quantum molecular rotor ground states
Nanomolecular assemblies of C 60 can be synthesized to enclose dipolar molecules.
Quantum Physics Disordered Systems and Neural Networks Chemical Physics
Discovering Symmetry Invariants and Conserved Quantities by Interpreting Siamese Neural Networks
It turns out that in the process of learning which datapoints belong to the same event or field configuration, these SNNs also learn the relevant symmetry invariants and conserved quantities.
Recurrent Neural Network Wavefunctions
A core technology that has emerged from the artificial intelligence revolution is the recurrent neural network (RNN).
Disordered Systems and Neural Networks Strongly Correlated Electrons Computational Physics Quantum Physics
Machine learning quantum states in the NISQ era
We review the development of generative modeling techniques in machine learning for the purpose of reconstructing real, noisy, many-qubit quantum states.
Quantum Physics Disordered Systems and Neural Networks Quantum Gases Strongly Correlated Electrons
Integrating Neural Networks with a Quantum Simulator for State Reconstruction
We demonstrate quantum many-body state reconstruction from experimental data generated by a programmable quantum simulator, by means of a neural network model incorporating known experimental errors.
Quantum Physics Quantum Gases
QuCumber: wavefunction reconstruction with neural networks
As we enter a new era of quantum technology, it is increasingly important to develop methods to aid in the accurate preparation of quantum states for a variety of materials, matter, and devices.
Quantum Physics Strongly Correlated Electrons
Deep Learning the Ising Model Near Criticality
We investigate this question by using unsupervised, generative graphical models to learn the probability distribution of a two-dimensional Ising system.
Learning Thermodynamics with Boltzmann Machines
A Boltzmann machine is a stochastic neural network that has been extensively used in the layers of deep architectures for modern machine learning applications.
Machine learning phases of matter
These results demonstrate the power of machine learning as a basic research tool in the field of condensed matter and statistical physics.
Strongly Correlated Electrons
Detecting Goldstone Modes with Entanglement Entropy
In the face of mounting numerical evidence, Metlitski and Grover [arXiv:1112. 5166] have given compelling analytical arguments that systems with spontaneous broken continuous symmetry contain a sub-leading contribution to the entanglement entropy that diverges logarithmically with system size.
Strongly Correlated Electrons High Energy Physics - Theory Quantum Physics
