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.
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.
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.
This study presents a comparative empirical evaluation of Variational Quantum Classifier (VQC) performance within the Quantum Machine Learning (QML) framework, focusing on VQC architectural analysis under NISQ-era qubit constraints. The VQC was designed utilizing ZFeatureMap for data encoding and EfficientSU2 as the ansatz. A systematic test was performed to evaluate the impact of three entanglement topologies (default, circular, pairwise) and two qubit scales (4 and 8) with four classical optimizers on standard classification datasets: Iris, Wine, and Breast Cancer. The results demonstrated that no single configuration is universally optimal. The default topology achieved the highest peak accuracy on multi-class datasets (Iris: 97% with P-BFGS), suggesting its efficiency in simpler parameter landscapes. Conversely, the pairwise and circular topologies showed superior stability and competitive accuracy (up to 92%) on the high-dimensional Breast Cancer dataset, confirming that richer qubit connectivity is essential for effective feature separation in more complex classification problems. Increasing the scale from 4 to 8 qubits was found to be crucial for improving the overall stability and consistency of performance rather than merely increasing peak accuracy. These findings provide essential empirical guidance for designing optimal and trainable VQC architectures under current quantum device limitations.
The rapid advancement of Noisy Intermediate-Scale Quantum (NISQ) devices necessitates a systematic evaluation of quantum machine learning (QML) algorithms to determine their feasibility for near-term applications. This study aims to benchmark the Variational Quantum Classifier (VQC)and the Quantum Kernel Support Vector Machine (QSVM) under realistic noise conditions modeled using Qiskit Aer. The primary objective is to investigate the impact of depolarizing and amplitude damping noise on the classification performance of these algorithms. Using the Iris binary classification task, we simulated four noise levels (0, 0.01, 0.05, 0.1) and evaluated accuracy across 1024 measurement shots. Results show that QSVM achieves superior accuracy (70%) under ideal and lownoise conditions, while VQC exhibits higher resilience at moderate noise (50% at 0.05 noise level) due to its adaptive Variational parameters. At higher noise levels (0.1), both models converge to ~35–40% accuracy, indicating significant degradation. These findings provide actionable insights for algorithm selection on NISQ hardware, with VQC favored for moderately noisy devices and QSVM preferable for low-noise or error-mitigated systems. The study concludes by highlighting the need for noise-aware ansatz design and error mitigation techniques to enhance QML performance in NISQ regimes.
The execution of Variational Quantum Algorithms on Noisy Intermediate-Scale Quantum hardware is severely impacted by hardware noise variations over time and decoherence. This work introduces an Adaptive Noise-Aware Variational Quantum Classifier (ANAVQC) that integrates Zero-Noise Extrapolation (ZNE) dynamically into the training loop to counter variations. The framework approximates the instantaneous depolarizing noise from calibration and adapts noise scaling and linear extrapolation to estimate noise-free expectation values at training. A two-qubit parameterized quantum circuit is built and simulated in MATLAB, with the inclusion of stochastic gradient descent coupled with parameter-shift-based gradients. The model is conditioned on a synthetic binary classification data under a time-varying depolarizing noise profile and exhibits improved convergence stability and classification accuracy over non-adaptive methods. The simulation results identify that adaptive ZNE increases model robustness against time-varying noise and offers a scalable route to reliable quantum machine learning on practical NISQ hardware. The current ANAVQC scheme is extendable to multi-qubit platforms and practical quantum backends for the purposes of future experimental testing.
This study presents a noise-aware evaluation framework for hybrid quantum–classical machine learning models applied to medical binary classification. Using three healthcare datasets—Pima Indians Diabetes, Heart Disease, and Breast Cancer Wisconsin—the performance of a Variational Quantum Classifier (VQC) was analyzed under multiple depolarizing noise levels (0.00–0.10). Classical baselines, Logistic Regression (LR) and Support Vector Machine (SVM), were implemented for comparison. The research investigates the impact of noise on predictive accuracy, F1-score, and training time, highlighting the computational overhead of hybrid quantum–classical models. Zero Noise Extrapolation (ZNE), using linear and Richardson methods, was applied to mitigate noise effects, revealing that while ZNE improves performance under certain conditions, extrapolation instability can occur. Transpiler optimization also contributed to improved VQC stability by reducing circuit complexity. Results show that classical models consistently outperformed the VQC in accuracy, stability, and training efficiency. VQC exhibited fluctuating performance under noise, requiring significantly higher training time, and increasing SPSA iterations did not always yield stable accuracy improvements. These findings underscore the challenges of noise robustness, optimization instability, and computational cost in the NISQ era, and suggest directions for future work, including alternative optimizers, adaptive ansatz design, advanced error mitigation, and real hardware benchmarking. This research provides a systematic benchmarking framework for evaluating training time versus noise-level trade-offs, contributing to the development of more reliable quantum machine learning models for medical applications. Keywords: Quantum Machine Learning, Variational Quantum Classifier, Depolarizing Noise, Zero Noise Extrapolation, Medical Classification, Hybrid Quantum–Classical Models, Training Time Analysis
Quantum Machine Learning (QML) has emerged as a compelling research domain that unites quantum computing principles with classical learning theory to address computationally demanding classification problems.This paper investigates two principal QML architectures-Variational Quantum Classifiers (VQC) and Quantum Neural Networks (QNN)-within the context of binary classification.We present an end-to-end hybrid quantum-classical pipeline applied to the UCI Mushroom dataset, comprising 22 categorical attributes, and demonstrate that quantumenhanced models can achieve competitive classification performance under present-day hardware constraints.The pipeline integrates Quantum Random Access Code (QRAC) encoding for dimensionality reduction, ZZFeatureMapbased quantum feature mapping, RealAmplitudes parameterized ansatz, and classical optimizer-driven parameter updates using the COBYLA algorithm.The complete system is deployed through a Flask web interface and containerized with Docker for reproducibility.Experimental results on a quantum simulator indicate a classification accuracy of 87.4%, precision of 0.891, and recall of 0.863, establishing the practical viability of NISQ-era quantum classifiers for structured categorical data.The paper further analyzes convergence behavior, circuit depth effects, and comparative performance against classical baselines, providing insights into the strengths and limitations of current QML architectures for real-world classification deployment.