一种用于条形探测器中径迹重建的量子计算方法
A Quantum Computing Approach to Track Reconstruction in Strip-Type Detectors
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中文总结 AI 辅助
研究利用量子退火解决条形气态探测器粒子径迹重建的组合优化问题，将重建子问题表述为二次无约束二元优化问题，经模拟测试，所提公式能重现局部重建决策，为量子与经典计算结合的重建方法研究提供基础。
中文摘要 AI 辅助
本研究探讨了量子退火在条形气态探测器中粒子径迹重建的应用。在此类探测器中，虚假击中与多重击中组合会使模式识别成为组合优化问题。我们将两个重建子问题表述为二次无约束二元优化问题。第一个子问题在局部候选区域内选择与单个光子径迹相关的探测器击中。第二个子问题从不同探测器层选择簇三元组，以便在单次量子处理单元提交中处理多个径迹候选。使用模拟的DAMSA探测器事件测试了所提出的公式。对于单径迹击中选择任务，基于量子处理单元的重建给出的位置和角度分辨率接近基于卡尔曼的重建。在同时关联任务中，首先从量子处理单元样本中提取有效簇三元组，然后使用基于图连通性的关联规则连接以构建径迹候选。本文研究的DAMSA事件拓扑具有低堆积且以类轴子粒子衰变的双光子信号为主。在此设置下，结果表明二次无约束二元优化公式可重现局部重建决策。这为在更复杂跟踪环境中进一步研究结合量子与经典计算的重建方法提供了实际基础。
英文摘要
This study investigates the use of quantum annealing for particle track reconstruction in strip-type gaseous detectors. In such detectors, ghost hits and multiple hit combinations can turn pattern recognition into a combinatorial optimization problem. We formulate two reconstruction subproblems as quadratic unconstrained binary optimization problems. The first subproblem selects detector hits associated with a single photon track inside a localized candidate region. The second subproblem selects cluster triplets from different detector layers so that multiple track candidates can be handled within a single quantum processing unit(QPU) submission. The proposed formulations are tested using simulated DAMSA detector events. For the single track hit selection task, the QPU based reconstruction gives position and angular resolutions close to those obtained with a Kalman based reconstruction. In the simultaneous association task, valid cluster triplets are first extracted from the QPU samples and then connected using an association rule based on graph connectivity to construct track candidates. The DAMSA event topology studied here has low pileup and is dominated by the two photon signal from axion-like particle(ALP) decay. In this setting, the results show that the QUBO formulations can reproduce local reconstruction decisions. This provides a practical basis for further studies of reconstruction methods that combine quantum and classical computing in more complex tracking environments.
