Cargando…
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...
Autores principales: | , , |
---|---|
Lenguaje: | eng |
Publicado: |
2023
|
Materias: | |
Acceso en línea: | http://cds.cern.ch/record/2853183 |
_version_ | 1780977190145359872 |
---|---|
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 |
record_format | invenio |
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 |
work_keys_str_mv | AT robbiatimatteo determiningprobabilitydensityfunctionswithadiabaticquantumcomputing AT cruzmartinezjuanm determiningprobabilitydensityfunctionswithadiabaticquantumcomputing AT carrazzastefano determiningprobabilitydensityfunctionswithadiabaticquantumcomputing |