no code implementations • 24 Sep 2024 • Yuxuan Zhang, Roeland Wiersema, Juan Carrasquilla, Lukasz Cincio, Yong Baek Kim

In this work, we explore the potential of a VQC scheme by making use of out-of-distribution generalization results in quantum machine learning (QML): By learning the action of a given many-body dynamics on a small data set of product states, we can obtain a unitary circuit that generalizes to highly entangled states such as the Haar random states.

1 code implementation • 30 May 2024 • Mohamed Hibat-Allah, Ejaaz Merali, Giacomo Torlai, Roger G Melko, Juan Carrasquilla

Rydberg atom array experiments have demonstrated the ability to act as powerful quantum simulators, preparing strongly-correlated phases of matter which are challenging to study for conventional computer simulations.

no code implementations • 27 Mar 2023 • Mohamed Hibat-Allah, Marta Mauri, Juan Carrasquilla, Alejandro Perdomo-Ortiz

In this study, we build over a proposed framework for evaluating the generalization performance of generative models, and we establish the first quantitative comparative race towards practical quantum advantage (PQA) between classical and quantum generative models, namely Quantum Circuit Born Machines (QCBMs), Transformers (TFs), Recurrent Neural Networks (RNNs), Variational Autoencoders (VAEs), and Wasserstein Generative Adversarial Networks (WGANs).

no code implementations • 20 Mar 2023 • Mohamed Hibat-Allah, Roger G. Melko, Juan Carrasquilla

Recurrent neural networks (RNNs), originally developed for natural language processing, hold great promise for accurately describing strongly correlated quantum many-body systems.

2 code implementations • 19 Jan 2023 • Juan Carrasquilla, Mohamed Hibat-Allah, Estelle Inack, Alireza Makhzani, Kirill Neklyudov, Graham W. Taylor, Giacomo Torlai

Binary neural networks, i. e., neural networks whose parameters and activations are constrained to only two possible values, offer a compelling avenue for the deployment of deep learning models on energy- and memory-limited devices.

1 code implementation • 28 Jul 2022 • Mohamed Hibat-Allah, Roger G. Melko, Juan Carrasquilla

We use symmetry and annealing to obtain accurate estimates of ground state energies of the two-dimensional (2D) Heisenberg model, on the square lattice and on the triangular lattice.

2 code implementations • 17 May 2021 • Elizabeth R. Bennewitz, Florian Hopfmueller, Bohdan Kulchytskyy, Juan Carrasquilla, Pooya Ronagh

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.

2 code implementations • 25 Jan 2021 • Mohamed Hibat-Allah, Estelle M. Inack, Roeland Wiersema, Roger G. Melko, Juan Carrasquilla

Many important challenges in science and technology can be cast as optimization problems.

no code implementations • 2 Dec 2020 • Matija Medvidovic, Juan Carrasquilla, Lauren E. Hayward, Bohdan Kulchytskyy

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 artificial neural networks.

Disordered Systems and Neural Networks Statistical Mechanics Computational Physics

1 code implementation • 22 Jun 2020 • Peter Cha, Paul Ginsparg, Felix Wu, Juan Carrasquilla, Peter L. McMahon, Eun-Ah Kim

Here we propose the "Attention-based Quantum Tomography" (AQT), a quantum state reconstruction using an attention mechanism-based generative network that learns the mixed state density matrix of a noisy quantum state.

1 code implementation • 3 Mar 2020 • Kyle Sprague, Juan Carrasquilla, Steve Whitelam, Isaac Tamblyn

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.

2 code implementations • 7 Feb 2020 • Mohamed Hibat-Allah, Martin Ganahl, Lauren E. Hayward, Roger G. Melko, Juan Carrasquilla

A core technology that has emerged from the artificial intelligence revolution is the recurrent neural network (RNN).

Disordered Systems and Neural Networks Strongly Correlated Electrons Computational Physics Quantum Physics

3 code implementations • 5 May 2016 • Juan Carrasquilla, Roger G. Melko

These results demonstrate the power of machine learning as a basic research tool in the field of condensed matter and statistical physics.

Strongly Correlated Electrons

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