Jul 2026· International Research Journal of Multidisciplinary Technovation· pp. 307-325· 0 citations· 22 references
TL;DR
A controlled optimizer-aware evaluation framework that integrates a fixed ZZFeatureMap–TwoLocal VQC architecture with callback-based convergence diagnostics to systematically analyse optimizer behaviour under identical experimental conditions is presented.
Abstract
Noisy Intermediate-Scale Quantum (NISQ) devices can be used to solve classification problems using a promising quantum machine learning technique called Variational Quantum Classifiers (VQC). The performance of VQCs is highly dependent on classical optimizers, and the understanding of the optimizer behavior within a given quantum architecture is limited. The proposed work is to present an Adaptive Optimizer-Enhanced Variational Quantum Classifier, which is a combination of a ZZFeatureMap for quantum data encoding and TwoLocal ansatz for variational learning, for solving non-linear binary classification problems. The proposed framework is tested using the Two Moons benchmark dataset, which is a popular benchmark for evaluating the ability of machine learning models to learn complex non-linear decision boundaries. The implementation is based on the Qiskit 1.2 and was run on a noiseless state vector simulator to remove the effects of hardware noise and focus on the effects of optimization. The three popular optimization techniques, namely ADAM, COBYLA and SPSA were explored under the same experimental setups. Besides the classification accuracy, a callback-based loss tracking mechanism was added to examine the speed of convergence, stability of optimization and evolution of losses during training. The experimental results showed that the overall accuracy of ADAM is 84.2%, which is the best, followed by COBYLA (79.3%) and SPSA (73.8%). Along with this, quantitative convergence analysis results showed that ADAM converged faster than the other optimizers and had lesser loss variance. It is shown that, with a proper optimization routine, a shallow two-qubit VQC can be used to successfully learn complex non-linear decision boundaries. The principal contribution of this work is a controlled optimizer-aware evaluation framework that integrates a fixed ZZFeatureMap–TwoLocal VQC architecture with callback-based convergence diagnostics to systematically analyse optimizer behaviour under identical experimental conditions.
Over The past decade, quantum computing and machine learning have each witnessed tremendous growth in their respective fields. While quantum computing promises a completely novel paradigm for computation, based on the principles of superposition, entanglement, and interference, machine learning has firmly cemented its place in the sphere of data-driven decision-making. This paper seeks to provide a practical solution to this question, implementing a quantum classical learning system that is capable of performing binary classification tasks. We achieve this through the creation of a Quantum Kernel Support Vector Classifier (QSVC) using the Qiskit framework. While the classical feature set from the Iris dataset is scaled appropriately for the quantum system, the feature set is then represented in the quantum state through the ZZFeatureMap circuit. The QSVC utilizes the kernel-based similarity between quantum states. The results were promising, with the QSVC obtaining a classification accuracy of 92% over a test set, with the model trained in 6.32 seconds on a simulated quantum backend. The decision boundaries learned by the model were complex, and the quantum kernel representation of the feature set was able to capture the underlying class structure, which would have been difficult to achieve in a classical low-dimensional feature space. Our method, with quantum circuits relegated to state preparation and measurement while leaving the optimization to classical processes, is designed to operate within these limits. This makes our method not only theoretically interesting but also immediately applicable to existing simulators and quantum hardware.
Hersh Kumar, Eslavath Hemanth Naik, Gadige Hemanth et al.· International Conference Com...· 0 citations
Quantum machine learning (QML) faces practical limitations due to noisy intermediate-scale quantum (NISQ) constraints, including noise, restricted qubit availability, and unstable optimization. This paper proposes HyQNet, a resource-aware hybrid quantum–classical framework designed to address these challenges through efficient circuit execution and adaptive optimization. The framework integrates optimized quantum circuits with classical learning strategies to improve scalability and stability under NISQ conditions. Experimental results on Iris, Wine, and Breast Cancer datasets show that HyQNet achieves an accuracy of 95.1% and F1-score of 94.8%, outperforming variational QNN (92.6%) and quantum SVM (91.2%). It also reduces runtime to 16.9 s compared to 20.5 s for VQNN, while maintaining efficient utilization of 8 qubits. Statistical analysis confirms significance (p < 0.05), and ablation studies validate the contribution of each component. The results demonstrate improved convergence stability and resource efficiency in hybrid quantum learning systems.
Sudheer Reddy K., Hastimal Jangid, Usha Desai· 2026 International Conferenc...· 0 citations
Quantum Machine Learning (QML) combines quantum computing principles and traditional machine learning principles to provide new ways to solve difficult classification problems through the use of quantum technology. The goal of this paper is to implement a Quantum Kernel Support Vector Machine (QKSVM) using PennyLane and evaluate it against a standard image classification dataset, Fashion-MNIST. The Fashion-MNIST images are represented as 28x28 grayscale pixel arrays; they are normalized using a min-max normalizer with output values between zero and one. The 28x28 arrays are then dimensionally reduced to a size of four principal components (approximately 85% cumulative explained variance), and the QKSVM will utilize a quantum circuit limited to four qubits which is consistent with today's Noisy, Intermediate-Scale Quantum (NISQ) capabilities for performing simulations. Each input feature is represented as a qubit via an RY rotation and the qubits are then entangled using a CNOT circuit to generate the quantum kernel matrix, which is computed as the pairwise fidelity of each input image's state, which is subsequently used by a classical SVM classifier implementing a one-versus-all classification method. The QKSVM achieved approximately 40% accuracy and a macro-F1 score of 0.37 on a 100 sample stratified subset of the Fashion-MNIST test dataset, compared with 60% and 91.2%, respectively, using a classical Radial Basis Function Support Vector Machine (RBF-SVM) classifier with the same four principal components of input features and a Convolutional Neural Network (CNN) with full input images. With a primary bottleneck of qubit count, circuit depth, and shot noise, this work serves as a fully documented and reproducible baseline for future research on quantifying the performance of hybrid classical and quantum image classifiers across standardized image dataset benchmarks.
M. Devi, S.Sravanthi, V. Chaithanya et al.· 2026 7th International Confe...· 0 citations
A hybrid quantum-classical convolutional neural network model (HQ-CNN) is presented and its performance on binary image classification tasks using subsets of the MNIST and EMNIST datasets is evaluated.
van-Nui Nguyen, Vu-Hai Nguyen, Q. Trần et al.· E3S Web of Conferences· 0 citations
The intersection of quantum processing and classical machine learning has spawned hybrid quantum-classical systems — practical system designs that attempt to use quantum potential in the limitations of modern Noisy Intermediate-Scale Quantum (NISQ) hardware. This paper introduces the development, deployment, and empirical analysis of a hybrid quantum-classical image classifier (which combines a Convolutional Neural Network (CNN) with an eight-qubit Variational Quantum Circuit (VQC)) in the binary classification of handwritten digits. This implementation, implemented in PennyLane and PyTorch, reaches a peak test accuracy of 99.85% across 1,984 test samples of the MNIST system, and only three errors are made. In addition to performance measures, the work presents an approach based on Quantum Software Engineering (QSE) by reporting major engineering issues, such as quantum-classical interface design, adjoint differentiation, feature-dimensionality reduction, and backend portability, and suggestion of seven quality-assurance practices of hybrid quantum software systems. The results demonstrate that it is possible to manufacture successfully hybrid quantum-classical architectures with the help of existing open-source tools and simulators, and provide future research and practice with hybrid QSE with effective advice.
Haider Ali, Muhammad Azeem Akbar, A. Khan et al.· SIGSOFT FSE Companion· 0 citations
Most of the used parameterized quantum circuit classifiers are created for binary problems and can later be generalized to multi-class problems via one-vs-all decomposition, which leads to a bigger number of models and higher costs of inference. In this paper, authors present Adaptive Non-Linear Quantum Neural Network (ANQNN) that performs direct classification of images into ten classes according to their eight-qubit statevector. Each 16×16 image is transformed into a vector of 256 features and divided into 32 sequential chunks of 8 features each that are processed sequentially. Prior to an encoding procedure, a special adaptive nonlinear transformation is applied to improve a simple linear term with trainable cubic and sinusoidal terms. Each chunk is encoded using the RY rotations and processed by means of subsequent chunk-specific RZ−RY−RZ rotations followed by controlled ring entanglement. During training, the adaptive angle of mixing influences the state of quantum mixture and at the end, 8 Pauli-Z expectation values are mapped to ten class logits via linear readout. The tests on MNIST and Fashion-MNIST datasets yielded 90.85% and 80.12% of accuracies respectively with mean one-vs-rest AUC values equaling to 0.9931 and 0.9748 respectively.The results indicate the viability of direct multi-class classification using sequential nonlinear feature encoding and parameterized quantum processing in the studied statevector setting, without asserting the existence of any general quantum computational advantage.
Prabha Bhumika Avasarala S V S S, Dr.Krishna Mohan A.· International Journal of Inn...· 0 citations