Speaker
Description
The optimization of tracking parameters in particle track reconstruction is a high-dimensional, non-convex problem with significant impact on tracking efficiency, resolution, and computational performance. As detector complexity and pileup increase, conventional heuristic and local optimization methods face scalability limitations. In this work, we will investigate quantum optimization techniques as alternative computing paradigms for tracking parameter tuning, focusing on both gate-based and annealing-based quantum approaches. We will formulate the tracking optimization problem as an energy minimization task and study implementations using the Quantum Approximate Optimization Algorithm (QAOA) on gate-based quantum processors, as well as quantum annealing based on Ising and Quadratic Unconstrained Binary Optimization (QUBO) formulations suitable for annealer hardware. We will discuss problem encoding strategies, cost function design, and practical considerations for mapping realistic tracking workflows to quantum optimization backends. Benchmarking against classical optimization methods will be used to assess solution quality, robustness, and scalability. This work aims to establish a unified framework for evaluating quantum optimization techniques in high energy physics and to explore their potential role in addressing long-standing combinatorial and computational challenges in tracking.