Abstract Quantum Computing and Machine Learning are both significant and appealing research fields. In particular, the combination of both has led to the emergence of the research field quantum machine learning which has recently taken enormous popularity. We investigate in the potential advantages of this synergy for the application in high energy physics, more precisely in reconstructing particle decay trees in particle collision experiments. Due to the larger computational space of quantum computers, this highly complex combinatorical problem is well suited for investigating in a potential quantum advantage compared to the classical scenario. However, current quantum devices are subject to noise and provide only a limited number of qubits. We therefore propose the utilization of a variational quantum circuit within a classical graph neural network which has been shown to be feasible for reconstruction of particle decay trees. We evaluate our approach on a quantum simulator and a real quantum computer by IBM Quantum for artificially generated decay trees and compare our results to the purely classical approach. Our proposed approach does not only enable hardware efficient utilization of nowadays quantum devices, but also shows competitive results even in the presence of noise.
Abstract Quantum Computing and Machine Learning are both significant and appealing research fields. In particular, the combination of both has led to the emergence of the research field quantum machine learning which has recently taken enormous popularity. We investigate in the potential advantages of this synergy for the application in high energy physics, more precisely in reconstructing particle decay trees in particle collision experiments. Due to the larger computational space of quantum computers, this highly complex combinatorical problem is well suited for investigating in a potential quantum advantage compared to the classical scenario. However, current quantum devices are subject to noise and provide only a limited number of qubits. We therefore propose the utilization of a variational quantum circuit within a classical graph neural network which has been shown to be feasible for reconstruction of particle decay trees. We evaluate our approach on a quantum simulator and a real quantum computer by IBM Quantum for artificially generated decay trees and compare our results to the purely classical approach. Our proposed approach does not only enable hardware efficient utilization of nowadays quantum devices, but also shows competitive results even in the presence of noise.
With the emergence of the research field of Quantum Machine Learning, interest in finding advantageous real-world applications is growing as well. However, challenges concerning the number of available qubits on Noisy Intermediate Scale Quantum (NISQ) devices and accuracy losses due to hardware imperfections still remain and limit the applicability of such approaches in real-world scenarios. Therefore, for simplification, most studies assume nearly noise-free conditions as they are expected with logical, i.e. error-corrected, qubits instead of real qubits provided by hardware. However, the number of logical qubits is expected to scale slowly as they require a high number of real qubits for error correction. This is our motivation to deal with noise as an unavoidable, non-negligible problem on NISQ devices. As an application, we use the example of particle decay tree reconstruction as a highly complex combinatoric problem in High Energy Physics. We investigate methods to reduce the noise impact of such devices and propose a hybrid architecture that extends a classical graph neural network by a parameterized quantum circuit. While we have shown that such a hybrid architecture enables a reduction of the amount of trainable parameters compared to the fully classical case, we are now specifically interested in the actual performance in more realistic, i.e. noise prone scenarios. Using simple synthetic Decay Trees, we train the network in classical simulations to allow for efficient optimization of the parameters. The trained parameters are validated in noisy simulations based on devices by "IBM Quantum" and are used in interpretability and significance studies, enabling improvements in the accuracy on real devices.
Tracking charged particles in high-energy physics experiments is a computationally intensive task. With the advent of the High Luminosity LHC era, which is expected to significantly increase the number of proton-proton interactions per beam collision, the amount of data to be analysed will increase dramatically. Traditional algorithms suffer from scaling problems. We are investigating the possibility of using machine learning techniques in combination with quantum computing. In our research, we represent particle trajectories as a graph data structure and train a hybrid graph neural network with classical and quantum layers. We present recent results on the application of these methods, focusing on the computational aspects of code development in different programming frameworks such as Jax, Pennylane and IBM Qiskit. We also provide insights into expected performance and explore the role of GPUs as computational accelerators in the simulation of quantum computing resources.
The High Luminosity Large Hadron Collider (HL-LHC) at CERN will involve a significant increase in complexity and sheer size of data with respect to the current LHC experimental complex. Hence, the task of reconstructing the particle trajectories will become more involved due to the number of simultaneous collisions and the resulting increased detector occupancy. Aiming to identify the particle paths, machine learning techniques such as graph neural networks are being explored in the HEP.TrkX project and its successor, the Exa.TrkX project. Both show promising results and reduce the combinatorial nature of the problem. Previous results of our team have demonstrated the successful attempt of applying quantum graph neural networks to reconstruct the particle track based on the hits of the detector. A higher overall accuracy is gained by representing the training data in a meaningful way within an embedded space. That has been included in the Exa.TrkX project by applying a classical MLP. Consequently, pairs of hits belonging to different trajectories are pushed apart while those belonging to the same ones stay close together. We explore the applicability of variational quantum circuits that include a relatively low number of qubits applicable to NISQ devices within the task of embedding and show preliminary results.
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.
The unprecedented increase of complexity and scale of data is expected in the necessary computation for tracking detectors of the High Luminosity Large Hadron Collider (HL-LHC) experiments. While currently used Kalman filter based algorithms are reaching their limits in terms of ambiguities from increasing number of simultaneous collisions, occupancy, and scalability (worse than quadratic), a variety of machine learning approaches to particle track reconstruction are explored. It has been demonstrated previously by HEP.TrkX using TrackML datasets, that graph neural networks, processing events as a graph connecting track measurements, are a promising solution and can reduce the combinatorial background to a manageable amount and are scaling to a computationally reasonable size. In previous work, we have shown a first attempt of Quantum Computing to Graph Neural Networks for track reconstruction of particles. We aim to leverage the capability of quantum computing to evaluate a very large number of states simultaneously and thus to effectively search in a large parameter space. As the next step in this paper, we present an improved model with an iterative approach to overcome the low accuracy convergence of the initial oversimplified Tree Tensor Network (TTN) model.
The high energy physics (HEP) community has a long history of dealing with large-scale datasets. To manage such voluminous data, classical machine learning and deep learning techniques have been employed to accelerate physics discovery. Recent advances in quantum machine learning (QML) have indicated the potential of applying these techniques in HEP. However, there are only limited results in QML applications currently available. In particular, the challenge of processing sparse data, common in HEP datasets, has not been extensively studied in QML models. This research provides a hybrid quantum-classical graph convolutional network (QGCNN) for learning HEP data. The proposed framework demonstrates an advantage over classical multilayer perceptron and convolutional neural networks in the aspect of number of parameters. Moreover, in terms of testing accuracy, the QGCNN shows comparable performance to a quantum convolutional neural network on the same HEP dataset while requiring less than 50% of the parameters. Based on numerical simulation results, studying the application of graph convolutional operations and other QML models may prove promising in advancing HEP research and other scientific fields.