Noise-Aware Quantum Support Vector Regression Under Simulator-Based NISQ Approximation

Quantum Support Vector Regression (QSVR) has become an exciting prospect of modeling the nonlinear relationship of high-dimensional data by using a kernel method. Nevertheless, the overall assessment of the whole QSVR pipeline, including preprocessing and dimensionality reduction, quantum embedding, shot-based kernel estimation, and noise-aware analysis, is not widespread. This paper introduces a working Python implementation of the QSVR framework that combines a principal component analysis (PCA)-to-qubit embedding, a 1024-shot quantum kernel implementation, and 1024-shot 1/2-SVR optimization on financial time-series and credit transaction regression problems. Classical features are normalized and minimized to around 20 major features that capture 95% of the variance in the data before being angle-based RY embedded using a depth-3 entangling circuit. Quantum kernels are built through state-overlap estimation on finite-shot constraints, and they are trained by cross-validated optimization. The robustness of the proposed framework is measured in both noise-free and simulator-generated noise conditions in case of controlled noise approximations. Experimentation indicates improved regression performance than that of an RBF-SVR baseline with the same preprocessing and validation conditions. The suggested workflow designates the concept of reproducibility, benchmarking control, and feasible noise-conscious evaluation, which offers a systematic reference structure to subsequent research within near-term quantum machine learning.