Abstract Variational Quantum Classifiers (VQCs) represent a promising approach forquantum-enhanced machine learning on Noisy Intermediate-Scale Quantum(NISQ) processors. This paper presents a comprehensive comparative studyinvestigating the impact of asymmetric readout errors on VQC performanceacross system scales. Through systematic numerical simulations of both 2-qubitand 4-qubit architectures trained on synthetic binary classification tasks, weanalyze how qubit role dependence and scalability interact with noise resilience.Our 2-qubit results demonstrate the classical optimizer’s remarkable capacityto compensate for measurement qubit noise, achieving 28.3% lower empiricalloss than theoretical predictions, while auxiliary qubit noise proves significantlymore detrimental. Extending to 4-qubit systems reveals persistent compensatorymechanisms, though with different quantitative patterns: 10% measurementqubit noise yields optimal accuracy (0.500 vs. 0.380 ideal), representing a 31.6%improvement. We observe consistent theoretical-empirical alignment across scales,with theoretical MSE values for 4-qubit systems ranging from 0.9423-0.9462for 5-20% error rates. The study demonstrates that while fundamental noiseresilience patterns persist during scaling, quantitative trade-offs and optimaloperating points shift, providing crucial insights for practical VQC deploymenton asymmetric NISQ hardware.
Abstract Variational Quantum Classifiers (VQCs) represent a promising approach forquantum-enhanced machine learning on Noisy Intermediate-Scale Quantum(NISQ) processors. This paper presents a comprehensive comparative studyinvestigating the impact of asymmetric readout errors on VQC performanceacross system scales. Through systematic numerical simulations of both 2-qubitand 4-qubit architectures trained on synthetic binary classification tasks, weanalyze how qubit role dependence and scalability interact with noise resilience.Our 2-qubit results demonstrate the classical optimizer’s remarkable capacityto compensate for measurement qubit noise, achieving 28.3% lower empiricalloss than theoretical predictions, while auxiliary qubit noise proves significantlymore detrimental. Extending to 4-qubit systems reveals persistent compensatorymechanisms, though with different quantitative patterns: 10% measurementqubit noise yields optimal accuracy (0.500 vs. 0.380 ideal), representing a 31.6%improvement. We observe consistent theoretical-empirical alignment across scales,with theoretical MSE values for 4-qubit systems ranging from 0.9423-0.9462for 5-20% error rates. The study demonstrates that while fundamental noiseresilience patterns persist during scaling, quantitative trade-offs and optimaloperating points shift, providing crucial insights for practical VQC deploymenton asymmetric NISQ hardware.
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.
This study investigates classification performance and trainability of variational quantum classifiers on near-term noisy quantum hardware across circuit depth and qubit count regimes within the context of quantum computing and machine learning, an area of growing scientific importance given its implications for hybrid quantum-classical algorithm design and practical NISQ hardware benchmarking for near-term quantum advantage assessment. Using gradient-based parameter optimization of parameterized quantum circuits with hardware noise modeling via density matrix simulation and physical execution on IBM Eagle processors, we examine expressibility of parameterized circuits enabling nonlinear decision boundaries in feature Hilbert space, with trainability constrained by barren plateau gradients at large qubit counts in 96 circuit architectures (4 qubit counts x 4 depths x 6 ansatz families) evaluated across 5 classification datasets with 20 random initialization seeds each drawn from IBM Quantum Eagle (127-qubit) and Falcon (27-qubit) processors with noise mitigation via zero-noise extrapolation. Results indicate that 10-qubit, 4-layer VQC achieves 84.2% classification accuracy on the best benchmark, within 3.8 percentage points of classical SVM, but gradient variance decays exponentially with qubit count (variance halving per 2 added qubits), confirming barren plateau scaling (p < 0.001), with 84.2% accuracy, 3.8 pp gap to classical SVM baseline as the primary quantitative benchmark. Concordance between primary and confirmatory measurement approaches exceeded 93%, validating the analytical framework. These findings contribute empirically to quantum computing and machine learning and carry actionable implications for the design of programs and policies targeting hybrid quantum-classical algorithm design and practical NISQ hardware benchmarking for near-term quantum advantage assessment.
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 limited supply of qubits and significant quantum noise impose limitations on the capability of quantum algorithms in the Noisy Intermediate-Scale Quantum (NISQ) era. NISQ devices have a variety of applications, such as Variational Quantum Circuit (VQC), which provides answers to difficult optimization and machine learning problems. This paper presents a thorough investigation of quantum variational classification in the NISQ context, with a focus on comprehending noise's impact on various feature maps and VQCs. We evaluate the effectiveness of quantum classifiers using a variety of datasets, ranging from straightforward binary classification problems to more complex tasks. Our results reveal the critical role that feature maps and variational circuit selection play in mitigating the effect of noise, identifying specific quantum circuit designs that exhibit robustness even in noisy situations. In order to highlight the potential of quantum machine learning in solving complex problems within in the NISQ setting, this study emphasizes the delicate interaction between feature map selection, variational circuit design, dataset complexity, and quantum noise.
This thesis presents a systematic empirical evaluation of quantum machine learning performance under noisy intermediate-scale quantum (NISQ) era constraints. Through 670 controlled experiments, it evaluated quantum kernel support vector machines and variational quantum classifiers against classical baselines on MNIST binary and multiclass classification tasks with systematic variation of problem difficulty, feature dimensionality (4, 8 qubits), and training set size (n ∈{100, 250, 400, 500, 2000, 4000}). Statistical rigor was ensured through five random seeds per condition and comprehensive significance testing. During the testing with binary datasets, classical methods (SVM, logistic regression, k-NN, neural networks) achieved 85.9% to 99.6% accuracy with training times under one second across all sample sizes. Quantum kernel methods utilizing ZZ feature maps yielded 70.5% to 96.0% accuracy, requiring 129 to 2,140 seconds training with quadratic sample size scaling. Larger scale quantum experiments (n = 4000 for both architectures, 8-qubit encoding with n = 2000 for quantum kernels) proved computationally impossible on available hardware leading to out of memory errors, highlighting practical NISQ-era scalability constraints. Variational quantum classifiers exhibited persistent performance stagnation, failing to exceed 66.5% accuracy despite increases in training data and feature dimensionality. Performance gaps ranged from 3.3 to 35.2 percentage points. All quantum classical differences achieved statistical significance (p< 0.05, with 13 of 14 comparisons meeting p< 0.01) and exhibited large to very large efect sizes (d> 1.0, with 13 of 14 comparisons exceeding d> 2.0). To further evaluate performance and scalability, a 4 class classification experiment was conducted following an identical experimental protocol, with training set sizes of n ∈{100, 250, 400}. Across 180 runs, classical baselines achieved mean accuracies between 87.2% and 95% across both 4 and 8 dimensional data. Quantum Kernel SVM struggled during runs with 4 dimensions, accuracy varying between 64.6% and 74%. Quantum Kernel SVM accuracy declined 16%-25% when the dimension and qubit count were increased to 8, showing findings of exponential kernel concentration. The Variational Quantum Classifier also performed poorly, with accuracy ranging from 43.4% to 44.6% across all runs, providing further evidence that it failed to leverage additional features or training data. Experiments employed noiseless statevector simulation, establishing that quantum underperformance stems from fundamental algorithmic limitations independent of hardware noise. A further extension to 8 class classification (digits 0–7) confirmed and amplified these findings: classical methods maintained 65–83% accuracy while Quantum Kernel SVM degraded to 34–47% and VQC collapsed to 21–23%, approaching the 12.5% random baseline. Supplementary appendix experiments further examined amplitude encoding on raw 784 dimensional pixel features and an alternative angle encoding scheme on the 8-class task; the latter demonstrated that replacing the ZZ Feature Map with simple per qubit rotations substantially recovered quantum kernel performance, suggesting feature map design as a more consequential variable than circuit depth or qubit count in near-term quantum classification.