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| Home > Publications database > Pattern Recognition of Particle Trajectories Using Quantum Computing for Future High-Energy Physics Experiments |
| Dissertation / PhD Thesis | PUBDB-2025-04402 |
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2025
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Please use a persistent id in citations: urn:nbn:de:bsz:25-freidok-2717009 doi:10.3204/PUBDB-2025-04402
Abstract: Quantum computing is an emerging interdisciplinary research field that is gaining increasing relevance in the 21st century. Quantum computers leverage unique quantum properties such as entanglement, superposition, and quantum interference to efficiently solve certain classes of computationally demanding tasks, where classical methods face significant limitations.An example is particle track reconstruction in collider experiments. Formulating the reconstruction task as a Quadratic Unconstrained Binary Optimisation enables compatibility with quantum computing frameworks such as gate-based variational algorithms tailored to handle this optimisation structure. In this thesis, the Variational Quantum Eigensolver, a classical-quantum hybrid algorithm based on the variational principle, is selected to solve the optimisation task. The results from numerical simulations of this algorithm are compared to those obtained using an exact classical method. A proof-of-concept implementation on real quantum hardware demonstrates the feasibility of the approach beyond numerical simulations.To investigate the potential of this quantum computing-based reconstruction in realistic scenarios, the approach is applied to simulations of two future high-energy physics experiments: the Laser Und XFEL Experiment, which probes Quantum Electrodynamics in strong electromagnetic fields, and a muon collider, designed to investigate various phenomena at the energy frontier.The track reconstruction performance is evaluated in a high-detector-occupancy environment, typical of certain settings within the Laser Und XFEL Experiment, and compared to established reconstruction methods. For the muon collider case study, the Quadratic Unconstrained Binary Optimisation is extended to a four-dimensional formulation that incorporates detector timing information. The impact of this enhancement on reconstruction performance is compared to that of the conventional three-dimensional approach, both for muons and slow-moving BSM charged particles.
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