86 citations · 116 across the 4 of their papers we have counts for
14 papers
Neural networks in quantum many-body physics: a hands-on tutorial
Juan Carrasquilla, Giacomo Torlai
Over the past years, machine learning has emerged as a powerful computational tool to tackle complex problems over a broad range of scientific disciplines. In particular, artificia…
Generative models for sampling of lattice field theories
Matija Medvidovic, Juan Carrasquilla, Lauren E. Hayward +1
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 a…
U(1) symmetric recurrent neural networks for quantum state reconstruction
Stewart Morawetz, Isaac J. S. De Vlugt, Juan Carrasquilla +1
Generative models are a promising technology for the enhancement of quantum simulators. These machine learning methods are capable of reconstructing a quantum state from experiment…
Exploring entanglement and optimization within the Hamiltonian Variational Ansatz
Roeland Wiersema, Cunlu Zhou, Yvette de Sereville +3
Quantum variational algorithms are one of the most promising applications of near-term quantum computers; however, recent studies have demonstrated that unless the variational quan…
Quantum process tomography with unsupervised learning and tensor networks
Giacomo Torlai, Christopher J. Wood, Atithi Acharya +3
The impressive pace of advance of quantum technology calls for robust and scalable techniques for the characterization and validation of quantum hardware. Quantum process tomograph…
Watch and learn -- a generalized approach for transferrable learning in deep neural networks via physical principles
Kyle Sprague, Juan Carrasquilla, Steve Whitelam +1
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. Such an approach has ena…