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Found 8 papers, 5 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.
Do Quantum Circuit Born Machines Generalize?
To the best of our knowledge, this is the first work in the literature that presents the QCBM's generalization performance as an integral evaluation metric for quantum generative models, and demonstrates the QCBM's ability to generalize to high-quality, desired novel samples.
Supplementing Recurrent Neural Networks with Annealing to Solve Combinatorial Optimization Problems
Combinatorial optimization problems can be solved by heuristic algorithms such as simulated annealing (SA) which aims to find the optimal solution within a large search space through thermal fluctuations.
Variational Neural Annealing
Many important challenges in science and technology can be cast as optimization problems.
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
