Reconstructing quantum states with generative models

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorMaRS Ctr-
Autor(es): dc.contributorUniv Waterloo-
Autor(es): dc.contributorPerimeter Inst Theoret Phys-
Autor(es): dc.contributorFlatiron Inst-
Autor(es): dc.contributorUniversidade Federal do Rio de Janeiro (UFRJ)-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorCarrasquilla, Juan-
Autor(es): dc.creatorTorlai, Giacomo-
Autor(es): dc.creatorMelko, Roger G.-
Autor(es): dc.creatorAolita, Leandro [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:56:33Z-
Data de disponibilização: dc.date.available2022-02-22T00:56:33Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2019-03-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1038/s42256-019-0028-1-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/209442-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/209442-
Descrição: dc.descriptionA major bottleneck in the development of scalable many-body quantum technologies is the difficulty in benchmarking state preparations, which suffer from an exponential 'curse of dimensionality' inherent to the classical description of quantum states. We present an experimentally friendly method for density matrix reconstruction based on neural network generative models. The learning procedure comes with a built-in approximate certificate of the reconstruction and makes no assumptions about the purity of the state under scrutiny. It can efficiently handle a broad class of complex systems including prototypical states in quantum information, as well as ground states of local spin models common to condensed matter physics. The key insight is to reduce state tomography to an unsupervised learning problem of the statistics of an informationally complete quantum measurement. This constitutes a modern machine learning approach to the validation of complex quantum devices, which may in addition prove relevant as a neural-network ansatz over mixed states suitable for variational optimization. Present day quantum technologies enable computations with tens and soon hundreds of qubits. A major outstanding challenge is to measure and benchmark the complete quantum state, a task that grows exponentially with the system size. Generative models based on restricted Boltzmann machines and recurrent neural networks can be employed to solve this quantum tomography problem in a scalable manner.-
Descrição: dc.descriptionPerimeter Institute for Theoretical Physics-
Descrição: dc.descriptionShared Hierarchical Academic Research Computing Network (SHARCNET)-
Descrição: dc.descriptionGovernment of Canada through Innovation, Science and Economic Development Canada-
Descrição: dc.descriptionProvince of Ontario through the Ministry of Economic Development, Job Creation and Trade-
Descrição: dc.descriptionNSERC of Canada-
Descrição: dc.descriptionCanada Research Chair-
Descrição: dc.descriptionAI grant-
Descrição: dc.descriptionCanada CIFAR AI (CCAI) Chairs Program-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ)-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionBrazilian agency Brazilian Serrapilheira Institute-
Descrição: dc.descriptionMaRS Ctr, Vector Inst Artificial Intelligence, Toronto, ON, Canada-
Descrição: dc.descriptionUniv Waterloo, Dept Phys & Astron, Waterloo, ON, Canada-
Descrição: dc.descriptionPerimeter Inst Theoret Phys, Waterloo, ON, Canada-
Descrição: dc.descriptionFlatiron Inst, Ctr Computat Quantum Phys, New York, NY USA-
Descrição: dc.descriptionUniv Fed Rio de Janeiro, Inst Fis, Rio De Janeiro, Brazil-
Descrição: dc.descriptionUNESP Univ Estadual Paulista, Inst Fis Teor, ICTP South Amer Inst Fundamental Res, Sao Paulo, Brazil-
Descrição: dc.descriptionUNESP Univ Estadual Paulista, Inst Fis Teor, ICTP South Amer Inst Fundamental Res, Sao Paulo, Brazil-
Descrição: dc.descriptionCNPq: 311416/2015-2-
Descrição: dc.descriptionFAPERJ: JCN E-26/202.701/2018-
Descrição: dc.descriptionCAPES: PROCAD2013-
Descrição: dc.descriptionBrazilian agency Brazilian Serrapilheira Institute: Serra-1709-17173-
Formato: dc.format155-161-
Idioma: dc.languageen-
Publicador: dc.publisherSpringer-
Relação: dc.relationNature Machine Intelligence-
???dc.source???: dc.sourceWeb of Science-
Título: dc.titleReconstructing quantum states with generative models-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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