用变分量子算法解决高能物理粒子轨迹重建难题
Variational Quantum Algorithms for Particle Track Reconstruction
- 设计可扩展的量子线路，针对固定探测器结构优化
- 在不同问题规模下验证算法性能与计算开销
- 为量子计算处理物理数据提供新思路，适合量子算法研究者
量子计算正快速发展，有望应对高能物理中日益严峻的计算挑战。本文探索变分量子算法在粒子轨迹重建问题中的潜力与局限性。针对多层探测系统中的直线轨迹识别问题（受LHCb顶点探测器启发），提出两种不同形式：一种是基态能量问题，另一种是线性方程组问题。针对变分量子算法在通用问题中难以设计高效表达性强的量子线路这一主要挑战，采用基于蒙特卡洛树搜索的量子架构搜索方法，为不同问题规模设计量子电路。通过实验评估两种方法在不同规模下的表现和计算成本。
原文摘要 · Abstract (English)
Quantum Computing is a rapidly developing field with the potential to tackle the increasing computational challenges faced in high-energy physics. In this work, we explore the potential and limitations of variational quantum algorithms in solving the particle track reconstruction problem. We present an analysis of two distinct formulations for identifying straight-line tracks in a multilayer detection system, inspired by the LHCb vertex detector. The first approach is formulated as a ground-state energy problem, while the second approach is formulated as a system of linear equations. This work addresses one of the main challenges when dealing with variational quantum algorithms on general problems, namely designing an expressive and efficient quantum ansatz working on tracking events with fixed detector geometry. For this purpose, we employed a quantum architecture search method based on Monte Carlo Tree Search to design the quantum circuits for different problem sizes. We provide experimental results to test our approach on both formulations for different problem sizes in terms of performance and computational cost.
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