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arXiv:2603.11968

From vacuum amplitudes to qubits

Germán Rodrigo

hep-phhep-exhep-thquant-phSubmitted Mar 12, 2026• Announced Mar 13, 2026• 0 citations
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Abstract

High-energy colliders, exemplified by the CERN's Large Hadron Collider (LHC), constitute genuine quantum machines. In alignment with Richard Feynman's foundational vision for quantum computing, collider physics emerge therefore as a prime candidate for quantum simulations. Prospective applications include Quantum Machine Learning for collider data analysis, accelerated evaluation of complex multiloop Feynman diagrams, efficient jet clustering, enhanced parton shower simulations, and related computational challenges. We discuss two specific applications: the identification of causal structures in multiloop vacuum amplitudes, a fundamental component of the Loop-Tree Duality exhibiting deep connections to graph theory; and high-dimensional function integration and sampling. The latter constitutes an initial step toward realizing a fully fleged quantum event generator capable of operating at high perturbative orders.

Comments
Presented at 17th International Symposium on Radiative Corrections: Applications of Quantum Field Theory to Phenomenology (RADCOR2025), 5-10 October 2025, Puri, India

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High-energy colliders, exemplified by the CERN's Large Hadron Collider (LHC), constitute genuine quantum machines. In alignment with Richard Feynman's foundational vision for quantum computing, collider physics emerge therefore as a prime candidate for quantum simulations. Prospective applications include Quantum Machine Learning for collider data analysis, accelerated evaluation of complex multiloop Feynman diagrams, efficient jet clustering, enhanced parton shower simulations, and related computational challenges. We discuss two specific applications: the identification of causal structures in multiloop vacuum amplitudes, a fundamental component of the Loop-Tree Duality exhibiting deep connections to graph theory; and high-dimensional function integration and sampling. The latter constitutes an initial step toward realizing a fully fleged quantum event generator capable of operating at high perturbative orders.
High-energy colliders, exemplified by the CERN's Large Hadron Collider (LHC), constitute genuine quantum machines. In alignment with Richard Feynman's foundational vision for quantum computing, collider physics emerge therefore as a prime candidate for quantum simulations. Prospective applications include Quantum Machine Learning for collider data analysis, accelerated evaluation of complex multiloop Feynman diagrams, efficient jet clustering, enhanced parton shower simulations, and related computational challenges. We discuss two specific applications: the identification of causal structures in multiloop vacuum amplitudes, a fundamental component of the Loop-Tree Duality exhibiting deep connections to graph theory; and high-dimensional function integration and sampling. The latter constitutes an initial step toward realizing a fully fleged quantum event generator capable of operating at high perturbative orders.
High-energy colliders, such as the Large Hadron Collider (LHC) at CERN, are genuine quantum machines, so, in line with Richard Feynman's original motivation for Quantum Computing, the scattering processes that take place there are natural candidates to be simulated on a quantum system. Potential applications range from quantum machine learning methods for collider data analysis, to faster and more precise evaluations of intricate multiloop Feynman diagrams, more efficient jet clustering, improved simulations of parton showers, and many other tasks. In this work, the focus will be on two specific applications: first, the identification of the causal structure of multiloop vacuum amplitudes, a key ingredient of the Loop-Tree Duality and an area with deep connections to graph theory; and second, the integration and sampling of high-dimensional functions. The latter constitutes a first step toward the realization of a fully fledged quantum event generator operating at high perturbative orders.
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 kkk-means, Affinity Propagation, and kTk_TkT​-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.
We motivate the use of quantum algorithms in particle physics and provide a brief overview of the most recent applications at high-energy colliders. In particular, we discuss in detail how a quantum approach reduces the complexity of jet clustering algorithms, such as anti-kT , and show how quantum algorithms efficiently identify causal configurations of multiloop Feynman diagrams. We also present a quantum integration algorithm, called QFIAE, which is successfully applied to the evaluation of one-loop Feynman integrals in a quantum simulator or in a real quantum device.
The efficient simulation of accurate collider data at modern collider experiments will be crucial for the detection of Beyond the Standard Model signatures in the search for new physics. Simulation of collision processes at hadron colliders like the LHC are carried out by Monte Carlo event generators which simulate the evolution from an initial hard scattering event, downwards in energy scales, towards the formation of long lived particles. However, as we enter a new era of higher luminosity experiments, accurate simulations of these pseudo-event generated data will become further computationally intensive with the vast amounts of data to generate. With the rapid development of quantum computing hardware, quantum computation offers itself as an alternative computing paradigm that may be harnessed to provide a natural framework to model several physical processes in high-energy physics, where the inherent quantum features of the device may be exploited to provide speedups or enhance current simulations. In this thesis, we present general and extendable quantum algorithms for two crucial parts of event generation: the calculation of matrix elements for the hard interaction and the QCD parton shower stage. First, a novel algorithm is proposed for the calculation of helicity amplitudes by outlining a proposal of constructing helicity spinors directly on a quantum circuit and manipulating the spinors to compute helicity amplitudes. This was used to calculate multiple helicity amplitudes for simple tree level scattering processes simultaneously as a proof-of-principle demonstration. The second algorithm outlines a proposal for a Monte Carlo-inspired parton shower algorithm which was used to simulate two shower steps of a simplified QCD model. The final algorithm extends the quantum parton shower algorithm onto a quantum walk framework which demonstrates significant scaling improvements, simulating more realistic shower depths. These algorithms utilise the quantum computers’ ability to remain in a quantum state throughout the computation and represents a first step towards a quantum computing algorithm describing full collision events at the LHC.
Quantum computing has emerged as a promising framework for addressing computationally demanding problems in collider physics. In recent years, a growing number of quantum algorithms have been proposed for applications ranging from event generation and parton shower simulation to the evaluation of scattering amplitudes, loop and phase-space integration, and optimization problems relevant to experimental analysis. We provide a concise overview of the main ideas behind these developments, with emphasis on the potential advantages of quantum approaches in comparison with classical methods, as well as on the current limitations imposed by noisy intermediate-scale quantum hardware.