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most citedMachine learning phases of matter

1.5k citations · 11.2k across the 149 of their papers we have counts for

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Showing 2019Show all

7 papers · 1 filter

quant-ph2019

Machine learning design of a trapped-ion quantum spin simulator

Yi Hong Teoh, Marina Drygala, Roger G. Melko +1

Trapped ions have emerged as one of the highest quality platforms for the quantum simulation of interacting spin models of interest to various fields of physics. In such simulators…

quant-ph2019★ 53 cited

The learnability scaling of quantum states: restricted Boltzmann machines

Dan Sehayek, Anna Golubeva, Michael S. Albergo +3

Generative modeling with machine learning has provided a new perspective on the data-driven task of reconstructing quantum states from a set of qubit measurements. As increasingly…

quant-ph2019★ 29 cited

Wavefunction positivization via automatic differentiation

Giacomo Torlai, Juan Carrasquilla, Matthew T. Fishman +2

We introduce a procedure to systematically search for a local unitary transformation that maps a wavefunction with a non-trivial sign structure into a positive-real form. The trans…

quant-ph2019★ 105 cited

Machine learning quantum states in the NISQ era

Giacomo Torlai, Roger G. Melko

We review the development of generative modeling techniques in machine learning for the purpose of reconstructing real, noisy, many-qubit quantum states. Motivated by its interpret…

cond-mat.str-el2019★ 2 cited

Universal divergence of the Renyi entropy of a thinly sliced torus at the Ising fixed point

Bohdan Kulchytskyy, Lauren E. Hayward Sierens, Roger G. Melko

The entanglement entropy of a quantum critical system can provide new universal numbers that depend on the geometry of the entangling bipartition. We calculate a universal number c…

quant-ph2019★ 143 cited

Integrating Neural Networks with a Quantum Simulator for State Reconstruction

Giacomo Torlai, Brian Timar, Evert P. L. van Nieuwenburg +9

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 e…

cond-mat.str-el2019★ 38 cited

Making Trotters Sprint: A Variational Imaginary Time Ansatz for Quantum Many-body Systems

Matthew J. S. Beach, Roger G. Melko, Tarun Grover +1

We introduce a variational wavefunction for many-body ground states that involves imaginary time evolution with two different Hamiltonians in an alternating fashion with variable t…