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arXiv:2601.03953

Quantum Monte Carlo Simulations for predicting electron-positron pair production via the linear Breit-Wheeler process

Lucas I. Iñigo Gamiz, Óscar Amaro, Efstratios Koukoutsis, Marija Vranić

physics.plasm-phquant-phSubmitted Jan 7, 2026• Announced Jan 8, 2026• 0 citations
arXivHTMLPDF

Abstract

Quantum computing (QC) has the potential to revolutionise the future of scientific simulations. To harness the capabilities that QC offers, we can integrate it into hybrid quantum-classical simulations, which can boost the capabilities of supercomputing by leveraging quantum modules that offer speedups over classical counterparts. One example is quantum Monte Carlo integration, which is theorised to achieve a quadratic speedup over classical Monte Carlo, making it suitable for high-energy physics, strong-field QED, and multiple scientific and industrial applications. In this paper, we demonstrate that quantum Monte Carlo can be used to predict the number of pairs created when two photon beams collide head-on, a problem relevant to high-energy physics and intense laser-matter interactions. The results from the quantum simulations demonstrate high accuracy relative to theoretical predictions. The accuracy of the simulations is only constrained by the approximations required to embed polynomials and to initialise the quantum state. We also demonstrate that our algorithm can be used in current quantum hardware, providing up to 90 % accuracy relative to theoretical predictions. Furthermore, we propose pathways towards integrations with classical simulation codes.

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Quantum computing (QC) has the potential to revolutionise the future of scientific simulations. To harness the capabilities that QC offers, we can integrate it into hybrid quantum-classical simulations, which can boost the capabilities of supercomputing by leveraging quantum modules that offer speedups over classical counterparts. One example is quantum Monte Carlo integration, which is theorised to achieve a quadratic speedup over classical Monte Carlo, making it suitable for high-energy physics, strong-field QED, and multiple scientific and industrial applications. In this paper, we demonstrate that quantum Monte Carlo can be used to predict the number of pairs created when two photon beams collide head-on, a problem relevant to high-energy physics and intense laser-matter interactions. The results from the quantum simulations demonstrate high accuracy relative to theoretical predictions. The accuracy of the simulations is only constrained by the approximations required to embed polynomials and to initialise the quantum state. We also demonstrate that our algorithm can be used in current quantum hardware, providing up to 90 % accuracy relative to theoretical predictions. Furthermore, we propose pathways towards integrations with classical simulation codes.
Abstract Quantum computers have a potential for solving quantum chemistry problems with higher accuracy than classical computers. Quantum computing quantum Monte Carlo (QC-QMC) is a QMC with a trial state prepared in quantum circuit, which is employed to obtain the ground state with higher accuracy than QMC alone. We propose an algorithm combining QC-QMC with a hybrid tensor network to extend the applicability of QC-QMC beyond a single quantum device size. In a two-layer quantum-quantum tree tensor, our algorithm for the larger trial wave function can be executed than preparable wave function in a device. Our algorithm is evaluated on the Heisenberg chain model, graphite-based Hubbard model, hydrogen plane model, and MonoArylBiImidazole using full configuration interaction QMC. Our algorithm can achieve energy accuracy (specifically, variance) several orders of magnitude higher than QMC, and the hybrid tensor version of QMC gives the same energy accuracy as QC-QMC when the system is appropriately decomposed. Moreover, we develop a pseudo-Hadamard test technique that enables efficient overlap calculations between a trial wave function and an orthonormal basis state. In a real device experiment by using the technique, we obtained almost the same accuracy as the statevector simulator, indicating the noise robustness of our algorithm. These results suggests that the present approach will pave the way to electronic structure calculation for large systems with high accuracy on current quantum devices.
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.
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.
High-energy physics simulations traditionally rely on classical Monte Carlo methods to model complex particle interactions, often incurring significant computational costs. In this paper, we introduce a novel quantum-enhanced simulation framework that integrates discrete-time quantum walks with quantum amplitude estimation to model photon interaction cross sections. By mapping the probabilistic transport process of 10 MeV photons in a water medium onto a quantum circuit and focusing on Compton scattering as the dominant attenuation mechanism, we demonstrate that our approach reproduces classical probability distributions with high fidelity. Simulation results obtained via the IBM Qiskit quantum simulator reveal a quadratic speedup in amplitude estimation compared to conventional Monte Carlo methods. Our framework not only validates the feasibility of employing quantum algorithms for high-energy physics simulations but also offers a scalable pathway toward incorporating multiple interaction channels and secondary particle production. These findings underscore the potential of quantum-enhanced methods to overcome the computational bottlenecks inherent in large-scale particle physics simulations.
Quantum field theory in the presence of strong background fields contains interesting problems where quantum computers may someday provide a valuable computational resource. In the noisy intermediate-scale quantum era it is useful to consider simpler benchmark problems in order to develop feasible approaches, identify critical limitations of current hardware, and build new simulation tools. Here we perform quantum simulations of strong-field QED (SFQED) in 3+1 dimensions, using real-time nonlinear Breit-Wheeler pair production as a prototypical process. The strong-field QED Hamiltonian is derived and truncated in the Furry-Volkov mode expansion, and the interactions relevant for Breit-Wheeler are transformed into a quantum circuit. Quantum simulations of a “null double slit” experiment are found to agree well with classical simulations following the application of various error mitigation strategies, including an asymmetric depolarization algorithm which we develop and adapt to the case of Trotterization with a time-dependent Hamiltonian. We also discuss longer-term goals for the quantum simulation of SFQED. Published by the American Physical Society 2024
Quantum computing and quantum Monte Carlo (QMC) are respectively the state-of-the-art quantum and classical computing methods for understanding many-body quantum systems. Here, we propose a hybrid quantum-classical algorithm that integrates these two methods, inheriting their distinct features in efficient representation and manipulation of quantum states and overcoming their limitations. We first introduce non-stoquasticity indicators (NSIs) and their upper bounds, which measure the sign problem, the most notable limitation of QMC. We show that our algorithm could greatly mitigate the sign problem, which decreases NSIs with the assistance of quantum computing. Meanwhile, the use of quantum Monte Carlo also increases the expressivity of shallow quantum circuits, allowing more accurate computation that is conventionally achievable only with much deeper circuits. We numerically test and verify the method for the N2_22​ molecule (12 qubits) and the Hubbard model (16 qubits). Our work paves the way to solving practical problems with intermediate-scale and early-fault tolerant quantum computers, with potential applications in chemistry, condensed matter physics, materials, high energy physics, etc.