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

• URL: http://arxiv.org/abs/2601.03953v1
• Date: Wed, 07 Jan 2026 14:07:38 GMT
• Title: Quantum Monte Carlo Simulations for predicting electron-positron pair production via the linear Breit-Wheeler process
• Authors: Lucas I. Iñigo Gamiz, Óscar Amaro, Efstratios Koukoutsis, Marija Vranić,
• Abstract summary: We demonstrate that quantum Monte Carlo can be used to predict the number of pairs created when two photon beams collide head-on.<n>The accuracy of the simulations is only constrained by the approximations required to embeds and to initialise the quantum state.<n>We also demonstrate that our algorithm can be used in current quantum hardware, providing up to 90 % accuracy relative to theoretical predictions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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