Comparative Analysis of Asymmetric Readout Errors on Variational Quantum Classifiers: Scaling from 2 to 4 Qubits

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.