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Found 11 papers, 8 papers with code
A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models
In this study, we build over a proposed framework for evaluating the generalization performance of generative models, and we establish the first quantitative comparative race towards practical quantum advantage (PQA) between classical and quantum generative models, namely Quantum Circuit Born Machines (QCBMs), Transformers (TFs), Recurrent Neural Networks (RNNs), Variational Autoencoders (VAEs), and Wasserstein Generative Adversarial Networks (WGANs).
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.
Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition
Binary neural networks, i. e., neural networks whose parameters and activations are constrained to only two possible values, offer a compelling avenue for the deployment of deep learning models on energy- and memory-limited devices.
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.
Neural Error Mitigation of Near-Term Quantum Simulations
Near-term quantum computers provide a promising platform for finding ground states of quantum systems, which is an essential task in physics, chemistry, and materials science.
Variational Neural Annealing
Many important challenges in science and technology can be cast as optimization problems.
Generative models for sampling of lattice field theories
We explore a self-learning Markov chain Monte Carlo method based on the Adversarial Non-linear Independent Components Estimation Monte Carlo, which utilizes generative models and artificial neural networks.
Disordered Systems and Neural Networks Statistical Mechanics Computational Physics
Attention-based Quantum Tomography
Here we propose the "Attention-based Quantum Tomography" (AQT), a quantum state reconstruction using an attention mechanism-based generative network that learns the mixed state density matrix of a noisy quantum state.
Watch and learn -- a generalized approach for transferrable learning in deep neural networks via physical principles
Transfer learning refers to the use of knowledge gained while solving a machine learning task and applying it to the solution of a closely related problem.
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 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
