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Solving the quantum many-body problem with artificial neural networks
TLDR
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
Machine learning (ML) encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. This
Neural-network quantum state tomography
TLDR
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.
Deep autoregressive models for the efficient variational simulation of many-body quantum systems
TLDR
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.
Two-dimensional frustrated J1−J2 model studied with neural network quantum states
TLDR
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.
Quantum Natural Gradient
TLDR
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.
Fermionic neural-network states for ab-initio electronic structure
TLDR
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.
Neural-Network Approach to Dissipative Quantum Many-Body Dynamics.
TLDR
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.
NetKet: A machine learning toolkit for many-body quantum systems
Nonstoquastic Hamiltonians and quantum annealing of an Ising spin glass
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
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