Charged particle tracking with quantum annealing optimization

Alexander Zlokapa,Daniel Lidar,Joshua Job,Maria Spiropulu,Jean-Roch Vlimant,Abhishek Anand,Javier M Duarte

doi:10.1007/s42484-021-00054-w

Abstract

At the High Luminosity Large Hadron Collider (HL-LHC), traditional track reconstruction techniques that are critical for physics analysis will need to be upgraded to scale with track density. Quantum annealing has shown promise in its ability to solve combinatorial optimization problems amidst an ongoing effort to establish evidence of a quantum speedup. As a step towards exploiting such potential speedup, we investigate a track reconstruction approach by adapting the existing geometric Denby-Peterson (Hopfield) network method to the quantum annealing framework for HL-LHC conditions. We develop additional techniques to embed the problem onto existing and near-term quantum annealing hardware. Results using simulated annealing and quantum annealing with the D-Wave 2X system on the TrackML open dataset are presented, demonstrating the successful application of a quantum annealing algorithm to the track reconstruction challenge. We find that combinatorial optimization problems can effectively reconstruct tracks, suggesting possible applications for fast hardware-specific implementations at the HL-LHC while leaving open the possibility of a quantum speedup for tracking.

Highlights

Track reconstruction is a critical and computationally intensive step for data analysis at high energy particle accelerator experiments (The HEP Software FoundationBatavia, IL 60510, USA 4 University of California San Diego, La Jolla, CA 92093, USA 5 Lockheed Martin Advanced Technology Center, Sunnyvale, CA 94089, USA 6 Departments of Electrical and Computer Engineering, Chemistry, and Physics & Astronomy, and Center for Quantum Information Science & Technology, University of Southern California, Los Angeles, CA 90089, USA2019)
We demonstrate one of the first big data applications of quantum annealing, reducing a large-scale problem to be successfully solved experimentally on D-Wave hardware with high purity and efficiency in tracking performance
We find that charged particle tracking can be successfully interpreted as a segment classification problem in a quadratic unconstrained binary optimization (QUBO) framework, using efficient classical pre-processing followed by quantum or simulated annealing

Summary

Introduction

Track reconstruction is a critical and computationally intensive step for data analysis at high energy particle accelerator experiments (The HEP Software FoundationBatavia, IL 60510, USA 4 University of California San Diego, La Jolla, CA 92093, USA 5 Lockheed Martin Advanced Technology Center, Sunnyvale, CA 94089, USA 6 Departments of Electrical and Computer Engineering, Chemistry, and Physics & Astronomy, and Center for Quantum Information Science & Technology, University of Southern California, Los Angeles, CA 90089, USA2019). In collider physics, tracks are crucial for a variety of measurements such as reconstruction of decay vertices (Collaboration 2014), identification of jet flavor (Chatrchyan and et al 2013; Sirunyan and et al 2018; Aad and et al 2016; Aaboud and et al 2018), pileup mitigation The High-Luminosity LHC (HL-LHC) upgrade, which is expected to be completed in 2026, will increase the number of simultaneous collisions (pileup) per proton bunch crossing from approxim to up to 200 (Apollinari et al 2017). Under these conditions, conventional algorithms, such as a Kalman filter, scale worse than quadratically with respect to the number of hits and are expected to require excessive computing resources (The HEP Software Foundation 2019). A variety of alternatives to current particle tracking methods are being pursued

Methods

Results

Conclusion