Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction

Abstract

The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC (HL-LHC). This increase in luminosity will significantly increase the number of particles interacting with the detector. The interaction of particles with a detector is referred to as "hit". The HL-LHC will yield many more detector hits, which will pose a combinatorial challenge by using reconstruction algorithms to determine particle trajectories from those hits. This work explores the possibility of converting a novel Graph Neural Network model, that can optimally take into account the sparse nature of the tracking detector data and their complex geometry, to a Hybrid Quantum-Classical Graph Neural Network that benefits from using Variational Quantum layers. We show that this hybrid model can perform similar to the classical approach. Also, we explore Parametrized Quantum Circuits (PQC) with different expressibility and entangling capacities, and compare their training performance in order to quantify the expected benefits. These results can be used to build a future road map to further develop circuit based Hybrid Quantum-Classical Graph Neural Networks.

20 pages, 18 figures

References in corpus (25)

• Array Programming with NumPy
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Variational Quantum Algorithms
• The power of quantum neural networks
• Noise-Induced Barren Plateaus in Variational Quantum Algorithms
• The effect of data encoding on the expressive power of variational quantum machine learning models
• Absence of Barren Plateaus in Quantum Convolutional Neural Networks
• Qulacs: a fast and versatile quantum circuit simulator for research purpose
• The Bitter Truth About Quantum Algorithms in the NISQ Era
• Estimating the gradient and higher-order derivatives on quantum hardware
• Application of Quantum Machine Learning using the Quantum Variational Classifier Method to High Energy Physics Analysis at the LHC on IBM Quantum Computer Simulator and Hardware with 10 qubits
• Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC
• Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculus
• MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
• Quantum Graph Neural Networks
• Noise-Assisted Quantum Autoencoder
• Training Saturation in Layerwise Quantum Approximate Optimisation
• Operator Sampling for Shot-frugal Optimization in Variational Algorithms
• Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics
• Hybrid Quantum-Classical Graph Convolutional Network
• Quantum speedup for track reconstruction in particle accelerators
• Towards a realistic track reconstruction algorithm based on graph neural networks for the HL-LHC
• Performance of Particle Tracking Using a Quantum Graph Neural Network
• Quantum Support Vector Machines for Continuum Suppression in B Meson Decays
• A Quantum Graph Neural Network Approach to Particle Track Reconstruction