Application of quantum machine learning using the quantum kernel algorithm on high energy physics analysis at the LHC

Wu, S. L.; Sun, S.; Guan, W.; Zhou, C.; Chan, J.; Cheng, C. L.; Pham, T.; Qian, Yan; Wang, Alex Zeng; Zhang, Rui; Livny, Miron; Glick, Jennifer R.; Barkoutsos, Panagiotis Kl.; Woerner, Stefan; Tavernelli, Ivano; Carminati, F.; Meglio, Alberto Di; Li, Andy; Lykken, J.; Spentzouris, P.; Chen, Samuel Yen-Chi; Yoo, Shinjae; Wei, Tzu-Chieh

doi:10.1103/physrevresearch.3.033221

Cited by 58 publications

(43 citation statements)

References 28 publications

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“…There are many problems investigated. This include but not limited to physics analysis at LHC using kernel (Wu et al 2021a;Heredge et al 2021) and variational methods (Wu et al 2021b;Terashi et al 2021), simulating parton showers (Jang et al 2021) and imitating calorimeter outputs using Quantum Generative Adversarial Networks (Chang et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Hybrid quantum classical graph neural networks for particle track reconstruction

Tüysüz

Rieger

Novotny³

et al. 2021

Quantum Mach. Intell.

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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.

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Section: Introductionmentioning

confidence: 99%

Hybrid quantum classical graph neural networks for particle track reconstruction

Tüysüz

Rieger

Novotny³

et al. 2021

Quantum Mach. Intell.

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“…Examples of data analyses on LHC, using supervised ML, can be found in a 2018 collaboration. 153 To take the potential advantage of quantum computers forward, quantum ML methods are also being investigated, see, for example, Wu et al., 154 and references therein, for proof-of-concept studies.…”

Section: Ai In Physicsmentioning

confidence: 99%

Artificial intelligence: A powerful paradigm for scientific research

et al. 2021

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Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

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“…It has been shown that quantum machine learning techniques can be used for the discrimination of interesting events from background [25][26][27]. Alternative applications of quantum algorithms within particle physics have also included particle track reconstruction, utilising both quantum annealers [4] and quantum neural networks [5].…”

Section: Introductionmentioning

confidence: 99%

Quantum Support Vector Machines for Continuum Suppression in B Meson Decays

Heredge

Hill

Hollenberg

et al. 2021

Comput Softw Big Sci

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Quantum computers have the potential to speed up certain computational tasks. A possibility this opens up within the field of machine learning is the use of quantum techniques that may be inefficient to simulate classically but could provide superior performance in some tasks. Machine learning algorithms are ubiquitous in particle physics and as advances are made in quantum machine learning technology there may be a similar adoption of these quantum techniques. In this work a quantum support vector machine (QSVM) is implemented for signal-background classification. We investigate the effect of different quantum encoding circuits, the process that transforms classical data into a quantum state, on the final classification performance. We show an encoding approach that achieves an average Area Under Receiver Operating Characteristic Curve (AUC) of 0.848 determined using quantum circuit simulations. For this same dataset the best classical method tested, a classical Support Vector Machine (SVM) using the Radial Basis Function (RBF) Kernel achieved an AUC of 0.793. Using a reduced version of the dataset we then ran the algorithm on the IBM Quantum ibmq_casablanca device achieving an average AUC of 0.703. As further improvements to the error rates and availability of quantum computers materialise, they could form a new approach for data analysis in high energy physics.

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Application of quantum machine learning using the quantum kernel algorithm on high energy physics analysis at the LHC

Cited by 58 publications

References 28 publications

Hybrid quantum classical graph neural networks for particle track reconstruction

Hybrid quantum classical graph neural networks for particle track reconstruction

Artificial intelligence: A powerful paradigm for scientific research

Quantum Support Vector Machines for Continuum Suppression in B Meson Decays

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