Cargando…

Quantum phase detection generalization from marginal quantum neural network models

Quantum machine learning offers a promising advantage in extracting information about quantum states, e.g., phase diagram. However, access to training labels is a major bottleneck for any supervised approach, preventing getting insights about new physics. In this Letter, using quantum convolutional...

Descripción completa

Detalles Bibliográficos
Autores principales: Monaco, Saverio, Kiss, Oriel, Mandarino, Antonio, Vallecorsa, Sofia, Grossi, Michele
Lenguaje:eng
Publicado: 2022
Materias:
Acceso en línea:https://dx.doi.org/10.1103/PhysRevB.107.L081105
http://cds.cern.ch/record/2848944
_version_ 1780976868294393856
author Monaco, Saverio
Kiss, Oriel
Mandarino, Antonio
Vallecorsa, Sofia
Grossi, Michele
author_facet Monaco, Saverio
Kiss, Oriel
Mandarino, Antonio
Vallecorsa, Sofia
Grossi, Michele
author_sort Monaco, Saverio
collection CERN
description Quantum machine learning offers a promising advantage in extracting information about quantum states, e.g., phase diagram. However, access to training labels is a major bottleneck for any supervised approach, preventing getting insights about new physics. In this Letter, using quantum convolutional neural networks, we overcome this limit by determining the phase diagram of a model where analytical solutions are lacking, by training only on marginal points of the phase diagram, where integrable models are represented. More specifically, we consider the axial next-nearest-neighbor Ising Hamiltonian, which possesses a ferromagnetic, paramagnetic, and antiphase, showing that the whole phase diagram can be reproduced.
id cern-2848944
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2022
record_format invenio
spelling cern-28489442023-10-15T06:23:56Zdoi:10.1103/PhysRevB.107.L081105http://cds.cern.ch/record/2848944engMonaco, SaverioKiss, OrielMandarino, AntonioVallecorsa, SofiaGrossi, MicheleQuantum phase detection generalization from marginal quantum neural network modelsquant-phGeneral Theoretical PhysicsQuantum machine learning offers a promising advantage in extracting information about quantum states, e.g., phase diagram. However, access to training labels is a major bottleneck for any supervised approach, preventing getting insights about new physics. In this Letter, using quantum convolutional neural networks, we overcome this limit by determining the phase diagram of a model where analytical solutions are lacking, by training only on marginal points of the phase diagram, where integrable models are represented. More specifically, we consider the axial next-nearest-neighbor Ising Hamiltonian, which possesses a ferromagnetic, paramagnetic, and antiphase, showing that the whole phase diagram can be reproduced.Quantum machine learning offers a promising advantage in extracting information about quantum states, e.g. phase diagram. However, access to training labels is a major bottleneck for any supervised approach, preventing getting insights about new physics. In this Letter, using quantum convolutional neural networks, we overcome this limit by determining the phase diagram of a model where analytical solutions are lacking, by training only on marginal points of the phase diagram, where integrable models are represented. More specifically, we consider the axial next-nearest-neighbor Ising (ANNNI) Hamiltonian, which possesses a ferromagnetic, paramagnetic and antiphase, showing that the whole phase diagram can be reproduced.arXiv:2208.08748oai:cds.cern.ch:28489442022-08-18
spellingShingle quant-ph
General Theoretical Physics
Monaco, Saverio
Kiss, Oriel
Mandarino, Antonio
Vallecorsa, Sofia
Grossi, Michele
Quantum phase detection generalization from marginal quantum neural network models
title Quantum phase detection generalization from marginal quantum neural network models
title_full Quantum phase detection generalization from marginal quantum neural network models
title_fullStr Quantum phase detection generalization from marginal quantum neural network models
title_full_unstemmed Quantum phase detection generalization from marginal quantum neural network models
title_short Quantum phase detection generalization from marginal quantum neural network models
title_sort quantum phase detection generalization from marginal quantum neural network models
topic quant-ph
General Theoretical Physics
url https://dx.doi.org/10.1103/PhysRevB.107.L081105
http://cds.cern.ch/record/2848944
work_keys_str_mv AT monacosaverio quantumphasedetectiongeneralizationfrommarginalquantumneuralnetworkmodels
AT kissoriel quantumphasedetectiongeneralizationfrommarginalquantumneuralnetworkmodels
AT mandarinoantonio quantumphasedetectiongeneralizationfrommarginalquantumneuralnetworkmodels
AT vallecorsasofia quantumphasedetectiongeneralizationfrommarginalquantumneuralnetworkmodels
AT grossimichele quantumphasedetectiongeneralizationfrommarginalquantumneuralnetworkmodels