Quantum computing applications in High Energy Physics: clustering, integration and generative models

TL;DR

This work investigates quantum computing as a tool for high-energy physics in three thrusts: quantum clustering for jet reconstruction, quantum Monte Carlo integration for multiloop Feynman calculations, and quantum generative models for fragmentation functions. It introduces novel quantum subroutines for Minkowski-distance computation and maximum-value search, along with a full end-to-end quantum Monte Carlo integrator (QFIAE) that combines a QNN Fourier decomposition with IQAE. The study applies these methods to Loop-Tree Duality representations, NLO decay rates, and fragmentation-function modeling, demonstrating feasibility on simulators and, in parts, on real hardware with modest depth and error-mitigation strategies. Collectively, the results show current quantum devices can address relevant HEP problems and lay groundwork for fault-tolerant quantum advantages in the future, signaling tangible progress toward quantum-accelerated analyses in particle physics.

Abstract

This PhD thesis explores the potential of quantum computing to address computational challenges in high-energy physics (HEP). As the Standard Model (SM) leaves key questions unanswered and no signs of new physics have emerged since the Higgs boson discovery, advanced experimental and computational tools become necessary. Quantum computing offers a promising alternative to classical methods, and this work investigates three main avenues where quantum algorithms can be useful in HEP research. First, we develop quantum subroutines for computing Minkowski distances and identifying maximum values in unsorted data, inserting them into clustering algorithms such as $k$-means, Affinity Propagation, and $k_T$-jet clustering. These quantum algorithms match classical performance while offering theoretical advantages when implemented on quantum hardware with qRAM. Then, we introduce a novel quantum Monte Carlo integrator, Quantum Fourier Iterative Amplitude Estimation (QFIAE), which combines a quantum neural network with amplitude estimation to integrate multivariate functions. QFIAE is executed on simulators and real quantum computers to evaluate Feynman loop integrals via Loop-Tree Duality, and is extended to compute (partially on hardware) a physical observable at NLO in perturbative quantum field theory. Finally, we present Quantum Chebyshev Probabilistic Models (QCPMs) for modeling multivariate distributions, applying them to fragmentation functions of partons fragmenting into kaons and pions. These models demonstrate accurate generative and interpolation capabilities. We also show how entanglement between variables plays a key role in training, enhancing model accuracy. Overall, these results show how quantum algorithms can already tackle relevant HEP problems on current hardware, while paving the way for future fault-tolerant applications that fully exploit quantum computational advantages.

Figures (69)

• Figure 1.1: Quantum circuit of the SwapTest.
• Figure 2.1: (a) Classification of the particles in the SM. Picture taken from wikiparticles. (b) Interactions among particles in the SM. Picture taken from wikiinterac.
• Figure 2.1: Sampling of fragmentation functions of partons into kaons $K^+$ and $K^-$.
• Figure 2.2: Sampling of fragmentation functions of partons into pions $\pi^+$ and $\pi^-$.
• Figure 3.1: Von Neumann entropies of the circuits in $z$ registers (top) and $Q$ registers (bottom) with (w/ CC) and without (w/o CC) correlations between $z$ and $Q$.