Solving the quantum many-body problem with artificial neural networks
A variational representation of quantum states based on artificial neural networks with a variable number of hidden neurons and a reinforcement-learning scheme that is capable of both finding the ground state and describing the unitary time evolution of complex interacting quantum systems.
Machine learning and the physical sciences
- G. Carleo, I. Cirac, Lenka Zdeborov'a
- Physics, Computer ScienceReviews of Modern Physics
- 25 March 2019
This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences, including conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields.
Neural-network quantum state tomography
- G. Torlai, G. Mazzola, J. Carrasquilla, M. Troyer, R. Melko, G. Carleo
- Physics, Computer Science
- 26 February 2018
It is demonstrated that machine learning allows one to reconstruct traditionally challenging many-body quantities—such as the entanglement entropy—from simple, experimentally accessible measurements, and can benefit existing and future generations of devices.
Quantum Natural Gradient
- J. Stokes, J. Izaac, N. Killoran, G. Carleo
- Computer ScienceQuantum
- 4 September 2019
An efficient algorithm is presented for computing a block-diagonal approximation to the Fubini-Study metric tensor for parametrized quantum circuits, which may be of independent interest.
Two-dimensional frustrated J1−J2 model studied with neural network quantum states
- Kenny Choo, T. Neupert, G. Carleo
- Computer Science, PhysicsPhysical review B
- 15 March 2019
This paper uses a fully convolutional neural network model as a variational ansatz to study the frustrated spin-1/2 J1-J2 Heisenberg model on the square lattice and demonstrates that the resulting predictions for both ground-state energies and properties are competitive with, and often improve upon, existing state-of-the-art methods.
Deep autoregressive models for the efficient variational simulation of many-body quantum systems
- Or Sharir, Yoav Levine, Noam Wies, G. Carleo, A. Shashua
- Computer SciencePhysical Review Letters
- 12 February 2019
This work proposes a specialized neural- network architecture that supports efficient and exact sampling, completely circumventing the need for Markov-chain sampling, and demonstrates the ability to obtain accurate results on larger system sizes than those currently accessible to neural-network quantum states.
Constructing exact representations of quantum many-body systems with deep neural networks
A technique to construct classical representations of many-body quantum systems based on artificial neural networks based on the deep Boltzmann machine architecture, based on which two layers of hidden neurons mediate quantum correlations are constructed.
Fermionic neural-network states for ab-initio electronic structure
- Kenny Choo, A. Mezzacapo, G. Carleo
- PhysicsNature Communications
- 27 September 2019
An extension of neural-network quantum states to model interacting fermionic problems and use neural-networks to perform electronic structure calculations on model diatomic molecules to achieve chemical accuracy.
Nonstoquastic Hamiltonians and quantum annealing of an Ising spin glass
- L. Hormozi, E. Brown, G. Carleo, M. Troyer
- Physics
- 21 September 2016
We study the role of Hamiltonian complexity in the performance of quantum annealers. We consider two general classes of annealing Hamiltonians: stoquastic ones, which can be simulated efficiently…
Neural-Network Approach to Dissipative Quantum Many-Body Dynamics.
- M. Hartmann, G. Carleo
- Physics, Computer SciencePhysical Review Letters
- 13 February 2019
This work represents the mixed many-body quantum states with neural networks in the form of restricted Boltzmann machines and derive a variational Monte Carlo algorithm for their time evolution and stationary states based on machine-learning techniques.
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