Charged particle tracking with quantum annealing-inspired optimization

Abstract

At the High Luminosity Large Hadron Collider (HL-LHC), traditional track reconstruction techniques that are critical for analysis are expected to face challenges due to scaling 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 and to HL-LHC conditions. Furthermore, 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 dataset are presented, demonstrating the successful application of a quantum annealing-inspired 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 LHC while leaving open the possibility of a quantum speedup for tracking.

18 pages, 21 figures

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Cited by in corpus (18)

• Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group
• The Tracking Machine Learning challenge : Throughput phase
• Comparing Three Generations of D-Wave Quantum Annealers for Minor Embedded Combinatorial Optimization Problems
• Quantum speedup for track reconstruction in particle accelerators
• Standard quantum annealing outperforms adiabatic reverse annealing with decoherence
• Counterdiabatic Reverse Annealing
• Fitting a Collider in a Quantum Computer: Tackling the Challenges of Quantum Machine Learning for Big Datasets
• Efficient Quantum Simulation of QCD Jets on the Light Front
• Deep learning optimal quantum annealing schedules for random Ising models
• Reconstructing charged particle track segments with a quantum-enhanced support vector machine
• Quantum-Annealing-Inspired Algorithms for Track Reconstruction at High-Energy Colliders
• Initial-State Dependent Optimization of Controlled Gate Operations with Quantum Computer
• Charged particle reconstruction for future high energy colliders with Quantum Approximate Optimization Algorithm
• Benchmarking Variational Quantum Algorithms for Combinatorial Optimization in Practice
• Quantum-annealing-inspired algorithms for multijet clustering
• Noise Effects on Diabatic Quantum Annealing Protocols
• TrackHHL: A Quantum Computing Algorithm for Track Reconstruction at the LHCb
• From Hope to Heuristic: Realistic Runtime Estimates for Quantum Optimisation in NHEP