Characterization and upgrade of a quantum graph neural network for charged particle tracking

TL;DR

This work characterize and upgrade a quantum graph neural network architecture for charged particle track reconstruction on a simulated high luminosity dataset, and presents new evidence of improved training behavior, specifically in terms of convergence toward the final trained configuration.

Abstract

In the forthcoming years the LHC experiments are going to be upgraded to benefit from the substantial increase of the LHC instantaneous luminosity, which will lead to larger, denser events, and, consequently, greater complexity in reconstructing charged particle tracks, motivating frontier research in new technologies. Quantum machine learning models are being investigated as potential new approaches to high energy physics (HEP) tasks. We characterize and upgrade a quantum graph neural network (QGNN) architecture for charged particle track reconstruction on a simulated high luminosity dataset. The model operates on a set of event graphs, each built from the hits generated in tracking detector layers by particles produced in proton collisions, performing a classification of the possible hit connections between adjacent layers. In this approach the QGNN is designed as a hybrid architecture, interleaving classical feedforward networks with parametrized quantum circuits. We characterize the interplay between the classical and quantum components. We report on the principal upgrades to the original design, and present new evidence of improved training behavior, specifically in terms of convergence toward the final trained configuration.

Figures (15)

• Figure 1: TrackML detector layout from trackml. The three different sub-detectors are shown on the left and with the associated colors in the z-r plane depiction on the right. Grey lines correspond to different pseudorapidity $\eta$ values.
• Figure 2: Message passing algorithm. Information from node A is first propagated to its nearest neighbor D, and then propagated to the next to nearest neighbors C and E.
• Figure 3: Block diagram of a graph neural network for edge classification. The iteration of Edge and Note Networks is responsible for the propagation of the information at different orders of proximity in the graph structure, as in Fig. \ref{['fig:Node messa']}.
• Figure 4: Structure of a generic hybrid sub-module in the QGNN, with encoding and readout layers to manage the dimensionality of the data, and a parametrized quantum circuit between classical networks. Arrows represent the data flow; no additional learnable parameters are present in-between different blocks, and the output of the previous block is directly taken as input to the next.
• Figure 5: The 3-layers and 4-qubits parametrized quantum circuit used as foundation to the original hybrid classical-quantum layers of the QGNN. Information is encoded in the initial quantum state through angle encoding. $RY$ gates perform $Y$-axis rotations, each parametrized by a trainable angle $\theta_i$