Quantum Machine Learning is a new computational tool that combines the quantum properties from quantum computing with the pattern recognition from machine learning. In this paper, we apply the Variational Quantum Classifier algorithm to the problem of pulsar classification of candidates from the High Time Resolution Universe 2 dataset. We use Qiskit Machine Learning circuits to compare the performance of the model using different feature selection methods, various number of features and training data size. Comparisons on the model from changing the data encoding and ansatz options are also reported. Keywords: Quantum Computing, Quantum Machine Learning, Astrophysics, Pulsars
Quantum Machine Learning is a new computational tool that combines the quantum properties from quantum computing with the pattern recognition from machine learning. In this paper, we apply the Variational Quantum Classifier algorithm to the problem of pulsar classification of candidates from the High Time Resolution Universe 2 dataset. We use Qiskit Machine Learning circuits to compare the performance of the model using different feature selection methods, various number of features and training data size. Comparisons on the model from changing the data encoding and ansatz options are also reported. Keywords: Quantum Computing, Quantum Machine Learning, Astrophysics, Pulsars
Quantum Machine Learning is a new computational tool that combines the quantum properties from quantum computing with the pattern recognition from machine learning. In this paper, we apply the Variational Quantum Classifier algorithm to the problem of pulsar classification of candidates from the High Time Resolution Universe 2 dataset. We use Qiskit Machine Learning circuits to compare the performance of the model using different feature selection methods, various number of features and training data size. Comparisons on the model from changing the data encoding and ansatz options are also reported.
Abstract Pulsar stars, characterized by their highly magnetized and rapidly rotating compact nature, as well as their emission of radiation beams from magnetic poles, play a crucial role in astrophysics, particularly in the study of gravitational waves and general relativity. This paper investigates the application of machine learning techniques for the classification of pulsars using radio wave emission data from the HTRU-2 dataset, which contains an extensive collection of candidate samples. The study explores two quantum-assisted methodologies: a multi-qubit Quantum Approximate Optimization Algorithm (QAOA) inspired encoding and an innovative single-qubit Quantum Asymptotically Universal Multi-feature (QAUM) encoding within a quantum neural network framework. The performance evaluation encompasses a range of metrics, including accuracy, precision, recall, and specificity, which are analyzed across various optimizer configurations and learning rates. Notably, the QAUM approach consistently demonstrates superior sensitivity and reduced error rates compared to QAOA, particularly evident at specific learning rate settings. These findings underscore the transformative potential of quantum computing in advancing pulsar detection methodologies, highlighting its efficacy and implications for astrophysical data analysis and the broader field of quantum-enhanced scientific exploration.
Quantum Machine Learning (QML) is a newly emerging research area at the intersection of classical machine learning (CML) and quantum computing (QC). Data is becoming voluminous rapidly, so it is challenging for classical computers to train Machine Learning (ML) models over massive datasets. The hope is that quantum physics' intrinsic features, such as entanglement, superposition, and interference, could be exploited as resources for training ML models on big datasets that would otherwise be relatively impossible for classical computers. It is theoretically proven that quantum computers have an exponential-time advantage over their classical counterparts in solving several problems, e.g., complex large dimensional matrix multiplication, factorization problem, unstructured database search, etc. QML models attempt to find a quantum advantage over their classical counterparts. Variational Quantum Classifiers (VQC) are hybrid quantum neural networks to perform the task of classification using QML models. VQC models in the present NISQ (Noisy Intermediate Scale Quantum — 50 to 100 qubits) era can produce comparable and even better results than Classical models. In this article, we examined the performance of a VQC while performing a simple binary classification task. In this article, we use a VQC to evaluate this method's performance empirically. We constructed a VQC to predict the label of fresh input for the typical Iris dataset comprising pairings of target outputs and training inputs. Our quantum classifier can reasonably predict species labels using only four qubits. These levels are fairly good compared to the accuracy levels attained by classical classifiers.
With the advancement of quantum computing in the Noisy Intermediate-Scale Quantum (NISQ) era, hybrid quantum-classical algorithms have emerged as promising approaches for addressing certain machine learning tasks. This study investigates the Variational Quantum Classifier (VQC), a hybrid model that integrates parameterized quantum circuits with classical optimization routines, implemented via IBM’s Qiskit framework. We provide a detailed account of the VQC architecture, including feature encoding, ansatz construction, and optimization methodology. To assess its performance, we conduct a comparative analysis between VQC and classical Support Vector Classifiers (SVC) across synthetic and real-world datasets, including Iris and a binary-filtered version of MNIST. Experimental results reveal that classical models consistently achieve higher accuracy and faster training times. However, the VQC demonstrates functional viability, particularly under noise-free conditions and reduced-dimensionality settings. These findings underscore the current limitations of quantum models while highlighting the potential of VQC as a proof-of-concept for quantum-enhanced learning as quantum hardware matures.
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.
Machine learning is the process of discovering patterns in data and using those patterns to forecast or predict future occurrences of those patterns. The machine learning process consists of three steps: input data management, model construction, and model evaluation. Combining machine learning and quantum computing allows for the design and operation of quantum circuits with behavior resembling that of classical machine learning. Variable quantum circuits are frequently used in quantum machine learning. Parameterized variables, a crucial part of variable quantum circuits, must be adjusted in order to achieve good performance of these circuits. It is possible to modify parameterized variables in a variety of ways, such as by using gradient methods or linear programming to determine the circuit’s optimal point. This study proposes a method for automatically designing a variational quantum circuit to classify the different datasets, including the well-known 1-dimensional and 2-dimensional datasets, iris and MNIST, using a genetic algorithm to find the best values for the parameterized variables. The Qiskit quantum simulation tools are employed in these experiments. The results show that genetic algorithms can find solutions with greater accuracy than linear-based optimizers by 94 percent and 37.5 percent, respectively, for the iris dataset and the MNIST dataset.