A Quantum Computing Approach to Track Reconstruction in Strip-Type Detectors

Particle track reconstruction in strip-type gaseous detectors is often hindered by ghost hits and combinatorial complexity, especially in high-multiplicity environments. This paper investigates whether quantum annealing, a quantum computing paradigm optimized for combinatorial problems, can accurately reconstruct particle tracks by mapping the reconstruction process to a Quadratic Unconstrained Binary Optimization (QUBO) framework.

The researchers formulated two distinct reconstruction subproblems as QUBO models:

1. Single track hit selection: Selecting the most geometrically consistent hits within a localized photon cluster candidate.
2. Simultaneous cluster association: Identifying valid cluster triplets across different detector layers to handle multiple track candidates in a single quantum processing unit (QPU) submission.

The formulations were tested using simulated events from the DAMSA (Dump-produced Axion-like particle search) experiment. The QUBO matrices were embedded onto D-Wave quantum annealers, and the resulting bitstrings were decoded to identify valid track candidates based on energy minimization and geometrical residuals.

For the single track hit selection task, the quantum-based reconstruction achieved position and angular resolutions comparable to traditional Kalman filter-based methods. In the simultaneous association task, the QUBO formulation successfully identified valid cluster triplets, which were then linked using graph connectivity rules to form complete track candidates. The results demonstrate that quantum annealing can effectively reproduce local reconstruction decisions in the low-pileup environment characteristic of the DAMSA experiment, providing a foundation for hybrid quantum-classical tracking algorithms.