Variational Quantum Algorithms for Particle Track Reconstruction

2025-11-14

AI Summary

This study investigates the applicability and limitations of variational quantum algorithms for reconstructing straight-line particle trajectories in multi-layer detector systems. To address this inverse tracking problem, we propose two quantum modeling paradigms: (i) ground-state estimation of a problem-encoded Hamiltonian and (ii) linear system solving via the HHL algorithm. For fixed detector geometries, we design parameter-efficient, physics-inspired quantum ansätze. Crucially, we introduce Monte Carlo Tree Search (MCTS) for automated quantum architecture search, dynamically tailoring ansatz structure to event multiplicity—enhancing expressivity while substantially reducing circuit depth and parameter count. Numerical experiments demonstrate complementary trade-offs between accuracy and resource overhead for the two approaches. Results confirm the feasibility of performing realistic high-energy physics track reconstruction on near-term, intermediate-scale quantum processors, establishing a novel pathway toward quantum-accelerated, real-time particle physics reconstruction.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Solving particle track reconstruction using variational quantum algorithms
• Designing expressive quantum circuits for fixed detector geometry
• Comparing ground-state energy and linear equation formulations for tracking

Innovation

• Variational quantum algorithms for particle tracking
• Ground-state energy and linear equations formulations
• Monte Carlo Tree Search for quantum circuit design