Abstract We report on a consistent comparison between techniques of quantum and classical machine learning applied to the classification of signal and background events for the Vector Boson Scattering processes, studied at the Large Hadron Collider installed at the CERN laboratory. Quantum machine learning algorithms based on variational quantum circuits are run on freely available quantum computing hardware, showing very good performances as compared to deep neural networks run on classical computing facilities. In particular, we show that such kind of quantum neural networks is able to correctly classify the targeted signal with an Area Under the characteristic Curve (AUC) that is very close to the one obtained with the corresponding classical neural network, but employing a much lower number of resources, as well as less variable data in the training set. Albeit giving a proof-of-principle demonstration with limited quantum computing resources, this work represents one of the first steps towards the use of near term and noisy quantum hardware for practical event classification in High Energy Physics experiments.
Abstract We report on a consistent comparison between techniques of quantum and classical machine learning applied to the classification of signal and background events for the Vector Boson Scattering processes, studied at the Large Hadron Collider installed at the CERN laboratory. Quantum machine learning algorithms based on variational quantum circuits are run on freely available quantum computing hardware, showing very good performances as compared to deep neural networks run on classical computing facilities. In particular, we show that such kind of quantum neural networks is able to correctly classify the targeted signal with an Area Under the characteristic Curve (AUC) that is very close to the one obtained with the corresponding classical neural network, but employing a much lower number of resources, as well as less variable data in the training set. Albeit giving a proof-of-principle demonstration with limited quantum computing resources, this work represents one of the first steps towards the use of near term and noisy quantum hardware for practical event classification in High Energy Physics experiments.
Abstract We report on a consistent comparison between techniques of quantum and classical machine learning applied to the classification of signal and background events for the Vector Boson Scattering processes, studied at the Large Hadron Collider installed at the CERN laboratory. Quantum machine learning algorithms based on variational quantum circuits are run on freely available quantum computing hardware, showing very good performances as compared to deep neural networks run on classical computing facilities. In particular, we show that such kind of quantum neural networks are able to correctly classify the signal with an Area Under the characteristic Curve (AUC) that is very close to the one obtained with the corresponding classical neural network, but employing a much lower number of resources, as well as less variable data in the training set. Albeit giving a proof-of-principle demonstration with limited quantum computing resources, this work represents one of the first steps towards the use of near term and noisy quantum hardware for practical event classification in High Energy Physics experiments.
Abstract We present studies of quantum algorithms exploiting machine learning to classify events of interest from background events, one of the most representative machine learning applications in high-energy physics. We focus on variational quantum approach to learn the properties of input data and evaluate the performance of the event classification using both simulators and quantum computing devices. Comparison of the performance with standard multi-variate classification techniques based on a boosted-decision tree and a deep neural network using classical computers shows that the quantum algorithm has comparable performance with the standard techniques at the considered ranges of the number of input variables and the size of training samples. The variational quantum algorithm is tested with quantum computers, demonstrating that the discrimination of interesting events from background is feasible. Characteristic behaviors observed during a learning process using quantum circuits with extended gate structures are discussed, as well as the implications of the current performance to the application in high-energy physics experiments.
A comparative study of variational quantum classifiers and classical machine learning for tW−ttˉ discrimination at the LHC
Precise measurements of single top-quark production in the associated tW-channel require efficient discrimination against the dominant ttˉ background. In this work, we investigate the applicability of quantum machine learning to this classification problem using the Variational Quantum Classifier (VQC) algorithm. Simulated proton-proton collision events from CMS Open Data corresponding to Run-2 are employed to train and evaluate the VQC model. Its performance is compared with two established classical approaches, Boosted Decision Trees (BDTs) and Multi-Layer Perceptron (MLP) using both machine learning metrics and physics-motivated metrics that describe the Signal Significance as a function of Signal Efficiency. The comparison is performed separately for the dileptonic e−μ channel and for the resolved and boosted hadronic topologies. In addition, signal-enriched and background-enriched phase-space regions are studied to assess the dependence of classifier performance on event topology. Our results provide a systematic benchmark for the application of VQC for a realistic High Energy Physics classification task and identify the analysis regimes wherein quantum-inspired models offer competitive discrimination with respect to established classical techniques.
The classification and regression of particle collision events constitute a persistent computational challenge in experimental high energy physics, where large volumes of simulated data must be processed with both speed and precision. This work carries out a systematic comparison of four classical machine learning architectures, support vector machines (SVM), artificial neural networks (ANN), convolutional neural networks (CNN), and long short-term memory (LSTM) networks against their quantum counterparts: quantum SVM (QSVM), quantum neural networks (QNN), quantum CNN (QCNN), and quantum LSTM (QLSTM). All models are trained on simulated proton-proton collision events with electron-positron and muon-antimuon final states from the CERN Open Data portal, using transverse-momentum components as input features and transverse-momentum magnitude as the regression target. Classical architectures, and in particular the CNN and LSTM, achieve marginally better quantitative performance under current hardware and dataset constraints. Quantum models, however, reach competitive accuracy with substantially fewer trainable parameters: the QCNN reproduces the performance of the deep classical CNN using only four qubits and a circuit of depth three, pointing to a genuine parameter-efficiency advantage on near-term quantum devices. A baseline analysis confirms that the regression problem is non-trivial for shallow polynomial fits, supporting the relevance of the architectural comparison. These results characterize the trade-offs between classical and quantum approaches under realistic, resource-constrained conditions and provide a benchmark for future studies on actual quantum hardware.
One of the major objectives of the experimental programs at the Large Hadron Collider (LHC) is the discovery of new physics. This requires the identification of rare signals in immense backgrounds. Using machine learning algorithms greatly enhances our ability to achieve this objective. With the progress of quantum technologies, quantum machine learning could become a powerful tool for data analysis in high energy physics. In this study, using IBM gate-model quantum computing systems, we employ the quantum variational classifier method in two recent LHC flagship physics analyses: (Higgs boson production in association with a top quark pair, probing the Higgs boson couplings to the top quark) and H → μ + μ − (Higgs boson decays to two muons, probing the Higgs boson couplings to second-generation fermions). We have obtained early results with 10 qubits on the IBM quantum simulator and the IBM quantum hardware. With small training samples of 100 events on the quantum simulator, the quantum variational classifier method performs similarly to classical algorithms such as SVM (support vector machine) and BDT (boosted decision tree), which are often employed in LHC physics analyses. On the quantum hardware, the quantum variational classifier method has shown promising discrimination power, comparable to that on the quantum simulator. This study demonstrates that quantum machine learning has the ability to differentiate between signal and background in realistic physics datasets. We foresee the usage of quantum machine learning in future high-luminosity LHC physics analyses, including measurements of the Higgs boson self-couplings and searches for dark matter.
Machine learning enjoys widespread success in High Energy Physics (HEP) analyses at LHC. However the ambitious HL-LHC program will require much more computing resources in the next two decades. Quantum computing may offer speed-up for HEP physics analyses at HL-LHC, and can be a new computational paradigm for big data analyses in High Energy Physics. We have successfully employed three methods (1) Variational Quantum Classifier (VQC) method, (2) Quantum Support Vector Machine Kernel (QSVM-kernel) method and (3) Quantum Neural Network (QNN) method for two LHC flagship analyses: ttH (Higgs production in association with two top quarks) and H->mumu (Higgs decay to two muons, the second generation fermions). We shall address the progressive improvements in performance from method (1) to method (3). We will present our experiences and results of a study on LHC High Energy Physics data analyses with IBM Quantum Simulator and Quantum Hardware (using IBM Qiskit framework), Google Quantum Simulator (using Google Cirq framework), and Amazon Quantum Simulator (using Amazon Braket cloud service). The work is in the context of a Qubit platform (a gate-model quantum computer). Taking into account the present limitation of hardware access, different quantum machine learning methods are studied on simulators and the results are compared with classical machine learning methods (BDT, classical Support Vector Machine and classical Neural Network). Furthermore, we do apply quantum machine learning on IBM quantum hardware to compare performance between quantum simulator and quantum hardware. The work is performed by an international and interdisciplinary collaboration with the Department of Physics and Department of Computer Sciences of University of Wisconsin, CERN Quantum Technology Initiative, IBM Research Zurich, IBM T.J. Watson Research Center, Fermilab Quantum Institute, BNL Computational Science Initiative, State University of New York at Stony Brook, and Quantum Computing and AI Research of Amazon Web Services. This work pioneers a close collaboration of academic institutions with industrial corporations in the High Energy Physics analyses effort. Though the size of event samples in future HL-LHC physics and the limited number of qubits pose some challenges to the Quantum Machine learning studies for High Energy Physics, more advanced quantum computers with larger number of qubits, reduced noise and improved running time (as envisioned by IBM and Google) may outperform classical machine learning in both classification power and in speed. Although the era of efficient quantum computing may still be years away, we have made promising progress and obtained preliminary results in applying quantum machine learning to High Energy Physics. A PROOF OF PRINCIPLE.