activity
20102025
most citedMachine learning phases of matter

1.5k citations · 5.7k across the 57 of their papers we have counts for

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Showing 2021Show all

7 papers · 1 filter

quant-ph2021★ 1 cited

Quasiprobabilistic state-overlap estimator for NISQ devices

Leonardo Guerini, Roeland Wiersema, Juan Felipe Carrasquilla +1

As quantum technologies mature, the development of tools for benchmarking their ability to prepare and manipulate complex quantum states becomes increasingly necessary. A key conce…

quant-ph2021★ 11 cited

Measurement-induced entanglement phase transitions in variational quantum circuits

Roeland Wiersema, Cunlu Zhou, Juan Felipe Carrasquilla +1

Variational quantum algorithms (VQAs), which classically optimize a parametrized quantum circuit to solve a computational task, promise to advance our understanding of quantum many…

quant-ph2021★ 46 cited

Optimal control of quantum thermal machines using machine learning

Ilia Khait, Juan Carrasquilla, Dvira Segal

Identifying optimal thermodynamical processes has been the essence of thermodynamics since its inception. Here, we show that differentiable programming (DP), a machine learning (ML…

quant-ph2021★ 62 cited

Neural Error Mitigation of Near-Term Quantum Simulations

Elizabeth R. Bennewitz, Florian Hopfmueller, Bohdan Kulchytskyy +2

Near-term quantum computers provide a promising platform for finding ground states of quantum systems, which is an essential task in physics, chemistry, and materials science. Near…

quant-ph2021★ 11 cited

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…

cond-mat.dis-nn2021★ 84 cited

Variational Neural Annealing

Mohamed Hibat-Allah, Estelle M. Inack, Roeland Wiersema +2

Many important challenges in science and technology can be cast as optimization problems. When viewed in a statistical physics framework, these can be tackled by simulated annealin…

cond-mat.dis-nn2021★ 6 cited

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…