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European Physical Society Conference on High Energy Physics (EPS-HEP)

Assessing the potential of quantum annealers for track reconstruction at LUXE

Annabel Kropf, Federico Meloni, Y. C. Yap

Published Dec 14, 2023• 0 citations
Publisher

Abstract

UXE (Laser Und XFEL Experiment) is a proposed DESY experiment using the European XFEL electron beam and a high-intensity laser. The experiment’s primary aim is to investigate the transition from the well-probed perturbative to the non-perturbative Quantum Electrodynamics regime. In LUXE, positrons are generated and directed towards a four-layered silicon pixel detector, with occupancies of up to 100 hits/mm2 for the initial phase. Reconstructing tracks from a substantial set of hits poses a significant challenge for classical computers. To address this challenge, the novel approach based on formulating the track pattern recognition task as a quadratic unconstrained binary optimisation (QUBO) problem is adopted. Classically, the expected performance of a quantum annealer for QUBO problems can be studied using Simulated Annealing. In this report, the performance of reconstructing tracks from LUXE using Simulated Annealing is benchmarked against results from a gate-based quantum computing simulator, as well as against the classical method of employing a Combinatorial Kalman Filter.

DOI
10.22323/1.449.0601

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Computing and Software for Big Science•Dec 1, 2023•14 citations
UXE (Laser Und XFEL Experiment) is a proposed DESY experiment using the European XFEL electron beam and a high-intensity laser. The experiment’s primary aim is to investigate the transition from the well-probed perturbative to the non-perturbative Quantum Electrodynamics regime. In LUXE, positrons are generated and directed towards a four-layered silicon pixel detector, with occupancies of up to 100 hits/mm2 for the initial phase. Reconstructing tracks from a substantial set of hits poses a significant challenge for classical computers. To address this challenge, the novel approach based on formulating the track pattern recognition task as a quadratic unconstrained binary optimisation (QUBO) problem is adopted. Classically, the expected performance of a quantum annealer for QUBO problems can be studied using Simulated Annealing. In this report, the performance of reconstructing tracks from LUXE using Simulated Annealing is benchmarked against results from a gate-based quantum computing simulator, as well as against the classical method of employing a Combinatorial Kalman Filter.
LUXE (Laser Und XFEL Experiment) is a proposed experiment at DESY which will study Quantum Electrodynamics (QED) in the strong-field regime, where QED becomes non-perturbative. Measuring the rate of created electron-positron pairs using a silicon pixel tracking detector is an essential ingredient to study this regime. Precision tracking of positrons traversing the four layers of the tracking detector becomes very challenging at high laser intensities due to the high rates, which can be computationally expensive for classical computers. In this work, we update our previous study of the potential of using quantum computing to reconstruct positron tracks. The reconstruction task is formulated as a quadratic unconstrained binary optimisation and is solved using simulated quantum computers and a hybrid quantum-classical algorithm, namely the variational quantum eigensolver. Different ansatz circuits and optimisers are studied. The results are discussed and compared with classical track reconstruction algorithms using a graph neural network and a combinatorial Kalman filter.
Abstract The LUXE experiment (LASER Und XFEL Experiment) is a new experiment in planning at DESY Hamburg, which will study Quantum Electrodynamics (QED) at the strong-field frontier. In this regime, QED is non-perturbative. This manifests itself in the creation of physical electron-positron pairs from the QED vacuum. LUXE intends to measure the positron production rate in this unprecedented regime by using, among others, a silicon tracking detector. The large number of expected positrons traversing the sensitive detector layers results in an extremely challenging combinatorial problem, which can become computationally very hard for classical computers. This paper presents a preliminary study to explore the potential of quantum computers to solve this problem and to reconstruct the positron trajectories from the detector energy deposits. The reconstruction problem is formulated in terms of a quadratic unconstrained binary optimisation. Finally, the results from the quantum simulations are discussed and compared with traditional classical track reconstruction algorithms.
LUXE (Laser Und XFEL Experiment) is a new experiment in planning in Hamburg, which will study Quantum Electrodynamics at the strong-field frontier. LUXE intends to measure the positron production rate in this unprecedented regime by using, among others, a silicon tracking detector. The large number of expected positrons traversing the sensitive detector layers results in an extremely challenging combinatorial problem, which can become computationally expensive for classical computers. We investigate the potential future use of gate-based quantum computers for pattern recognition in track reconstruction, based on a quadratic unconstrained binary optimisation and a quantum graph neural network. This report introduces these approaches and compares their performance with a classical track reconstruction algorithm.
The LUXE experiment is a new experiment in planning in Hamburg, which will study quantum electrodynamics at the strong-field frontier. LUXE intends to measure the positron production rate in this unprecedented regime using, among others, a silicon tracking detector. The large number of expected positrons traversing the sensitive detector layers results in an extremely challenging combinatorial problem, which can become computationally expensive for classical computers. This paper investigates the potential future use of gate-based quantum computers for pattern recognition in track reconstruction. Approaches based on a quadratic unconstrained binary optimisation and a quantum graph neural network are investigated in classical simulations of quantum devices and compared with a classical track reconstruction algorithm. In addition, a proof-of-principle study is performed using quantum hardware.
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.
Abstract The LUXE experiment is a new experiment in planning in Hamburg, which will study quantum electrodynamics at the strong-field frontier. LUXE intends to measure the positron production rate in this unprecedented regime using, among others, a silicon tracking detector. The large number of expected positrons traversing the sensitive detector layers results in an extremely challenging combinatorial problem, which can become computationally expensive for classical computers. This paper investigates the potential future use of gate-based quantum computers for pattern recognition in track reconstruction. Approaches based on a quadratic unconstrained binary optimisation and a quantum graph neural network are investigated in classical simulations of quantum devices and compared with a classical track reconstruction algorithm. In addition, a proof-of-principle study is performed using quantum hardware.