Supersymmetry (SUSY) is a proposed hypothetical extension of the Standard Model (SM) that relates bosons and fermions differing in spin by one-half. Certain SUSY models may help address the hierarchy problem, provide a dark-matter candidate, and achieve gauge-coupling unification. Quantum machine learning (QML) has been investigated as a possible tool for high-energy physics (HEP) event classification, including the discrimination of SUSY signal from SM background. Rather than assessing the general feasibility of variational quantum classifiers, this study performs a strict, parameter-matched comparison between a classical Artificial Neural Network (ANN) and a classically simulated Quantum Neural Network (QNN), also considering training time as a point of comparison. Both models were trained on the same 18 input features from a 5000-event subset of the SUSY benchmark dataset, split 70/15/15 into stratified training, validation and test sets. The compact classical ANN (18→3→1 architecture) contained 61 trainable parameters; the QNN---amplitude embedding on five qubits, six variational blocks of RY - RX rotations, and a trainable classical output layer---contained 66 trainable parameters. Each model was optimised using the Adam optimiser with a learning rate of 1*10^{-3} and binary cross-entropy loss for 200 epochs, repeated across 100 independent training runs, each containing a different split sample. Mean test metrics were highly comparable: the QNN achieved marginally higher accuracy (79.0% vs 78.9%), recall and F1 score, while the ANN achieved higher precision, higher AUC (0.858 vs. 0.857) and lower loss (0.455 vs. 0.459), with no metric differing by more than 0.8 percentage points. However, the ANN completed its 100 training runs in 20 minutes, versus around 5 hours for the classically simulated QNN, taking almost 15 times as long to perform the same task. These results do not imply a quantum advantage, but confirm that a compact variational QNN can learn to distinguish SUSY signal from SM background at a level comparable to that of a classical model, but at a computational cost that makes it far less efficient.
Supersymmetry (SUSY) is a proposed hypothetical extension of the Standard Model (SM) that relates bosons and fermions differing in spin by one-half. Certain SUSY models may help address the hierarchy problem, provide a dark-matter candidate, and achieve gauge-coupling unification. Quantum machine learning (QML) has been investigated as a possible tool for high-energy physics (HEP) event classification, including the discrimination of SUSY signal from SM background. Rather than assessing the general feasibility of variational quantum classifiers, this study performs a strict, parameter-matched comparison between a classical Artificial Neural Network (ANN) and a classically simulated Quantum Neural Network (QNN), also considering training time as a point of comparison. Both models were trained on the same 18 input features from a 5000-event subset of the SUSY benchmark dataset, split 70/15/15 into stratified training, validation and test sets. The compact classical ANN (18→3→1 architecture) contained 61 trainable parameters; the QNN---amplitude embedding on five qubits, six variational blocks of RY - RX rotations, and a trainable classical output layer---contained 66 trainable parameters. Each model was optimised using the Adam optimiser with a learning rate of 1*10^{-3} and binary cross-entropy loss for 200 epochs, repeated across 100 independent training runs, each containing a different split sample. Mean test metrics were highly comparable: the QNN achieved marginally higher accuracy (79.0% vs 78.9%), recall and F1 score, while the ANN achieved higher precision, higher AUC (0.858 vs. 0.857) and lower loss (0.455 vs. 0.459), with no metric differing by more than 0.8 percentage points. However, the ANN completed its 100 training runs in 20 minutes, versus around 5 hours for the classically simulated QNN, taking almost 15 times as long to perform the same task. These results do not imply a quantum advantage, but confirm that a compact variational QNN can learn to distinguish SUSY signal from SM background at a level comparable to that of a classical model, but at a computational cost that makes it far less efficient.
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.
This study presents a performance analysis of Quantum Neural Networks (QNN) versus classical benchmarks. We compare three architectures: Classical Artificial Neural Networks (ANN), Variational QNNs (VQNN), and Post-Variational QNNs (Post-QNN) across standard classification datasets (PIMA Diabetes, Heart Disease, and Iris). While VQNNs are theoretically powerful, our experiments reveal significant challenges in hyperparameter tuning and stability, often resulting in suboptimal accuracy. Conversely, the Post-QNN approach—which leverages fixed quantum feature maps and classical optimization—demonstrates superior stability. Our results show that Post-QNNs not only outperform VQNNs by wide margins (e.g., +20% accuracy on Heart Disease) but also match or exceed classical ANN performance. This research highlights the efficacy of Post-Variational methods in overcoming the trainability barriers of standard parameterized quantum circuits.
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 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.
Quantum Machine Learning (QML) has emerged as an interdisciplinary field that harnesses the potential of quantum computing to revolutionize classical machine learning paradigms. This paper presents a benchmarking study focused on four quantum classifier algorithms: Quantum Support Vector Classifier (QSVC), Variational Quantum Circuit (VQC), Quantum Neural Network Classifier (QNNC) and Pegasos Quantum Support Vector Classifier (PegasosQSVC). These quantum classifiers hold promise for unlocking quantum advantages in classification tasks. However, a systematic assessment of their performance across diverse datasets is yet to be fully explored. In this research study, we evaluate and compare the classification performance of quantum machine learning classifiers on various datasets (Breast Cancer, Diabetes, and Titanic) and compare them to a classical model. Our findings revealed that PegasosQSVC consistently maintained high performance across all datasets with the highest average accuracy, recall, and F1-score values of 0.81, 0.72, and 0.71, respectively, followed by VQC, followed by VQC, while QSVC and QNNC perform variably. Nevertheless, QSVC was found to balance performance and training time, achieving an average accuracy of 0.81 and a training time of 177 seconds. Overall, the classical model surpassed QML models as it was up to 10% higher in average accuracy.
Event triggering sits at the heart of high-energy physics, where the rare events of interest must be retained while an overwhelming background is discarded under tight latency and bandwidth budgets. This work compares four classical machine learning models, namely a support vector machine, an artificial neural network, a convolutional network and a long short-term memory network, with four hybrid quantum counterparts, on a trigger-like binary classification task built from CMS open data. The label is defined by an invariant-mass window, and the inputs combine reconstructed kinematics with physics-motivated derived variables: the pseudorapidity difference, the wrapped azimuthal difference, the angular separation and the total transverse momentum. The quantum models run under a fixed resource budget of eight qubits, a principal-component compression to sixteen features and state-vector simulation. Every model shares the same stratified split, the same preprocessing and a common decision threshold, and performance is reported through accuracy, ROC-AUC, F1-score, precision and recall. The strongest classical model is the artificial neural network, at 93.53 percent accuracy and 0.9819 ROC-AUC, while the strongest quantum model is the quantum convolutional network, at 90.89 percent accuracy and 0.9731 ROC-AUC, with the quantum neural network close behind. The quantum-kernel and recurrent quantum approaches trail both, which places the trainable hybrid embeddings ahead within this budget. The study is meant as a controlled reference point rather than a claim of quantum advantage.