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Found 9 papers, 6 papers with code
Recurrent neural network wave functions for Rydberg atom arrays on kagome lattice
Rydberg atom array experiments have demonstrated the ability to act as powerful quantum simulators, preparing strongly-correlated phases of matter which are challenging to study for conventional computer simulations.
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.
Provably efficient machine learning for quantum many-body problems
In this work, we prove that classical ML algorithms can efficiently predict ground state properties of gapped Hamiltonians in finite spatial dimensions, after learning from data obtained by measuring other Hamiltonians in the same quantum phase of matter.
Precise measurement of quantum observables with neural-network estimators
The measurement precision of modern quantum simulators is intrinsically constrained by the limited set of measurements that can be efficiently implemented on hardware.
Quantum Physics Disordered Systems and Neural Networks Strongly Correlated Electrons
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
NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems
We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques.
Quantum Physics Disordered Systems and Neural Networks Strongly Correlated Electrons Computational Physics Data Analysis, Statistics and Probability
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
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.
