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.
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.
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 (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.
Abstract One-class classification (OCC) is a fundamental problem in pattern recognition with a wide range of applications. This work presents a semi-supervised quantum machine learning algorithm for such a problem, which we call a variational quantum one-class classifier (VQOCC). The algorithm is suitable for noisy intermediate-scale quantum computing because the VQOCC trains a fully-parameterized quantum autoencoder with a normal dataset and does not require decoding. The performance of the VQOCC is compared with that of the one-class support vector machine (OC-SVM), the kernel principal component analysis (PCA), and the deep convolutional autoencoder (DCAE) using handwritten digit and Fashion-MNIST datasets. The numerical experiment examined various structures of VQOCC by varying data encoding, the number of parameterized quantum circuit layers, and the size of the latent feature space. The benchmark shows that the classification performance of VQOCC is comparable to that of OC-SVM and PCA, although the number of model parameters grows only logarithmically with the data size. The quantum algorithm outperformed DCAE in most cases under similar training conditions. Therefore, our algorithm constitutes an extremely compact and effective machine learning model for OCC.
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.
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.