Search Results for author: Marta Mauri

Found 4 papers, 1 papers with code

A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models

no code implementations27 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).

Quantum Machine Learning

Do Quantum Circuit Born Machines Generalize?

no code implementations27 Jul 2022 Kaitlin Gili, Mohamed Hibat-Allah, Marta Mauri, Chris Ballance, Alejandro Perdomo-Ortiz

To the best of our knowledge, this is the first work in the literature that presents the QCBM's generalization performance as an integral evaluation metric for quantum generative models, and demonstrates the QCBM's ability to generalize to high-quality, desired novel samples.

valid

Generalization Metrics for Practical Quantum Advantage in Generative Models

no code implementations21 Jan 2022 Kaitlin Gili, Marta Mauri, Alejandro Perdomo-Ortiz

Using the sample-based approach proposed here, any generative model, from state-of-the-art classical generative models such as GANs to quantum models such as Quantum Circuit Born Machines, can be evaluated on the same ground on a concrete well-defined framework.

Tensor Networks valid

NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems

1 code implementation29 Mar 2019 Giuseppe Carleo, Kenny Choo, Damian Hofmann, James E. T. Smith, Tom Westerhout, Fabien Alet, Emily J. Davis, Stavros Efthymiou, Ivan Glasser, Sheng-Hsuan Lin, Marta Mauri, Guglielmo Mazzola, Christian B. Mendl, Evert van Nieuwenburg, Ossian O'Reilly, Hugo Théveniaut, Giacomo Torlai, Alexander Wietek

We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques.

Quantum Physics Disordered Systems and Neural Networks Strongly Correlated Electrons Computational Physics Data Analysis, Statistics and Probability

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