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Determining probability density functions with adiabatic quantum computing

A reliable determination of probability density functions from data samples is still a relevant topic in scientific applications. In this work we investigate the possibility of defining an algorithm for density function estimation using adiabatic quantum computing. Starting from a sample of a one-di...

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Detalles Bibliográficos
Autores principales: Robbiati, Matteo, Cruz-Martinez, Juan M., Carrazza, Stefano
Lenguaje:eng
Publicado: 2023
Materias:
Acceso en línea:http://cds.cern.ch/record/2853183
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author Robbiati, Matteo
Cruz-Martinez, Juan M.
Carrazza, Stefano
author_facet Robbiati, Matteo
Cruz-Martinez, Juan M.
Carrazza, Stefano
author_sort Robbiati, Matteo
collection CERN
description A reliable determination of probability density functions from data samples is still a relevant topic in scientific applications. In this work we investigate the possibility of defining an algorithm for density function estimation using adiabatic quantum computing. Starting from a sample of a one-dimensional distribution, we define a classical-to-quantum data embedding procedure which maps the empirical cumulative distribution function of the sample into time dependent Hamiltonian using adiabatic quantum evolution. The obtained Hamiltonian is then projected into a quantum circuit using the time evolution operator. Finally, the probability density function of the sample is obtained using quantum hardware differentiation through the parameter shift rule algorithm. We present successful numerical results for predefined known distributions and high-energy physics Monte Carlo simulation samples.
id cern-2853183
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2023
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spelling cern-28531832023-10-17T02:09:00Zhttp://cds.cern.ch/record/2853183engRobbiati, MatteoCruz-Martinez, Juan M.Carrazza, StefanoDetermining probability density functions with adiabatic quantum computinghep-phParticle Physics - Phenomenologyquant-phGeneral Theoretical PhysicsA reliable determination of probability density functions from data samples is still a relevant topic in scientific applications. In this work we investigate the possibility of defining an algorithm for density function estimation using adiabatic quantum computing. Starting from a sample of a one-dimensional distribution, we define a classical-to-quantum data embedding procedure which maps the empirical cumulative distribution function of the sample into time dependent Hamiltonian using adiabatic quantum evolution. The obtained Hamiltonian is then projected into a quantum circuit using the time evolution operator. Finally, the probability density function of the sample is obtained using quantum hardware differentiation through the parameter shift rule algorithm. We present successful numerical results for predefined known distributions and high-energy physics Monte Carlo simulation samples.arXiv:2303.11346TIF-UNIMI-2023-9CERN-TH-2023-042oai:cds.cern.ch:28531832023-03-20
spellingShingle hep-ph
Particle Physics - Phenomenology
quant-ph
General Theoretical Physics
Robbiati, Matteo
Cruz-Martinez, Juan M.
Carrazza, Stefano
Determining probability density functions with adiabatic quantum computing
title Determining probability density functions with adiabatic quantum computing
title_full Determining probability density functions with adiabatic quantum computing
title_fullStr Determining probability density functions with adiabatic quantum computing
title_full_unstemmed Determining probability density functions with adiabatic quantum computing
title_short Determining probability density functions with adiabatic quantum computing
title_sort determining probability density functions with adiabatic quantum computing
topic hep-ph
Particle Physics - Phenomenology
quant-ph
General Theoretical Physics
url http://cds.cern.ch/record/2853183
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