1Quantum Research Centre, Technology Innovation Institute, Abu Dhabi, UAE
2Departament de Física Quàntica i Astrofísica og Institut de Ciències del Cosmos (ICCUB), Universitat de Barcelona, Barcelona, Spanien.
3Teoretisk fysikafdeling, CERN, CH-1211 Geneve 23, Schweiz.
4Institut for Fysik, National Yang Ming Chiao Tung Universitet, Hsinchu 30010, Taiwan.
5TIF Lab, Dipartimento di Fisica, Università degli Studi di Milano og INFN Sezione di Milano, Milano, Italien.
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Abstrakt
Vi foreslår og vurderer en alternativ kvantegeneratorarkitektur i sammenhæng med generativ adversarial læring til Monte Carlo-hændelsesgenerering, der bruges til at simulere partikelfysiske processer ved Large Hadron Collider (LHC). Vi validerer denne metode ved at implementere kvantenetværket på kunstige data genereret fra kendte underliggende distributioner. Netværket anvendes derefter på Monte Carlo-genererede datasæt af specifikke LHC-spredningsprocesser. Den nye kvantegeneratorarkitektur fører til en generalisering af de avancerede implementeringer, der opnår mindre Kullback-Leibler-divergenser selv med netværk med lav dybde. Desuden lærer kvantegeneratoren med succes de underliggende distributionsfunktioner, selvom den trænes med små træningsprøvesæt; dette er især interessant for dataforstærkningsapplikationer. Vi implementerer denne nye metodologi på to forskellige kvantehardwarearkitekturer, fanget-ion og superledende teknologier, for at teste dens hardwareuafhængige levedygtighed.
Udvalgt billede: Til venstre, skematiske trin involveret i stil-qGAN-træningen. Til højre, marginale stikprøvefordelinger for de fysiske observerbare, s, t, y i pp -> ttbar-produktion ved LHC for stil-qGAN-modellen.
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Citeret af
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