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Quantum Transfer Learning as a Noise Diagnostic Tool: Using Classical-to-Quantum Feature Handoff to Characterize Decoherence Sensitivity in Parameterized Quantum Circuits

Dr. Zuhair Ahmed

Published Jan 1, 2026• 0 citations
Publisher

Abstract

Abstract Quantum machine learning algorithms hold promise for near-term quantum advantage, yet their performance under realistic hardware noise remains poorly characterized. In this work, we introduce a hybrid classical-to-quantum transfer learning architecture in which a fixed linear encoder maps twodimensional classical input data to four quantum rotation angles, which are subsequently encoded into a parameterized quantum circuit (PQC) ansatz executed on the PKTRON v3.7.3 simulation framework using the PK NoisyLab 8Q virtual device. We systematically evaluate the degradation of binary classification accuracy and the quantum feature space under three distinct decoherence channels — amplitude damping, phase damping, and depolarizing noise — across five noise strength levels (p = 0.01 to 0.35) and three entanglement depths (L = 1, 2, 3 layers). Our results reveal that phase damping and depolarizing noise impose an immediate, strength-invariant accuracy reduction of 10% from the ideal baseline of 83.33%, while amplitude damping exhibits partial robustness at low noise levels. Frobenius distance analysis demonstrates that depolarizing noise induces the steepest degradation of the quantum feature space, reaching a saturation plateau at p = 0.20. Critically, deeper entanglement circuits achieve higher ideal accuracy but exhibit superlinear growth in Frobenius distance under noise, suggesting a fundamental accuracy-robustness tradeoff. We propose a Noise Sensitivity Score S(N) as a lightweight, application-level noise diagnostic metric, offering a tractable alternative to full quantum process tomography for NISQ-era devices.

DOI
10.21203/rs.3.rs-9577498/v1

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Abstract Quantum machine learning algorithms hold promise for near-term quantum advantage, yet their performance under realistic hardware noise remains poorly characterized. In this work, we introduce a hybrid classical-to-quantum transfer learning architecture in which a fixed linear encoder maps twodimensional classical input data to four quantum rotation angles, which are subsequently encoded into a parameterized quantum circuit (PQC) ansatz executed on the PKTRON v3.7.3 simulation framework using the PK NoisyLab 8Q virtual device. We systematically evaluate the degradation of binary classification accuracy and the quantum feature space under three distinct decoherence channels — amplitude damping, phase damping, and depolarizing noise — across five noise strength levels (p = 0.01 to 0.35) and three entanglement depths (L = 1, 2, 3 layers). Our results reveal that phase damping and depolarizing noise impose an immediate, strength-invariant accuracy reduction of 10% from the ideal baseline of 83.33%, while amplitude damping exhibits partial robustness at low noise levels. Frobenius distance analysis demonstrates that depolarizing noise induces the steepest degradation of the quantum feature space, reaching a saturation plateau at p = 0.20. Critically, deeper entanglement circuits achieve higher ideal accuracy but exhibit superlinear growth in Frobenius distance under noise, suggesting a fundamental accuracy-robustness tradeoff. We propose a Noise Sensitivity Score S(N) as a lightweight, application-level noise diagnostic metric, offering a tractable alternative to full quantum process tomography for NISQ-era devices.
Quantum machine learning algorithms hold promise for near-term quantum advantage, yet theirperformance under realistic hardware noise remains poorly characterized. In this work, we introduce ahybrid classical-to-quantum transfer learning architecture in which a fixed linear encoder maps twodimensional classical input data to four quantum rotation angles, which are subsequently encoded into aparameterized quantum circuit (PQC) ansatz executed on the PKTRON v3.7.3 simulation frameworkusing the PK NoisyLab 8Q virtual device. We systematically evaluate the degradation of binaryclassification accuracy and the quantum feature space under three distinct decoherence channels —amplitude damping, phase damping, and depolarizing noise — across five noise strength levels (p = 0.01to 0.35) and three entanglement depths (L = 1, 2, 3 layers). Our results reveal that phase damping anddepolarizing noise impose an immediate, strength-invariant accuracy reduction of 10% from the idealbaseline of 83.33%, while amplitude damping exhibits partial robustness at low noise levels. Frobeniusdistance analysis demonstrates that depolarizing noise induces the steepest degradation of the quantumfeature space, reaching a saturation plateau at p = 0.20. Critically, deeper entanglement circuits achievehigher ideal accuracy but exhibit superlinear growth in Frobenius distance under noise, suggesting afundamental accuracy-robustness tradeoff. We propose a Noise Sensitivity Score S(N) as a lightweight,application-level noise diagnostic metric, offering a tractable alternative to full quantum processtomography for NISQ-era devices.
Near-term quantum machine learning (QML) models operate in environments wherein noise is unavoidable, arising from both imperfect classical data acquisition and the limitations of noisy intermediate-scale quantum (NISQ) hardware. Although most existing studies have focused primarily on quantum circuit noise in isolation, the combined influence of corrupted classical inputs and quantum hardware noise has received comparatively little attention. In this work, we present a systematic experimental study of the robustness of a variational quantum classifier under realistic multi-level noise conditions. Using the Titanic dataset as a benchmark, a range of dataset-level noise models-including speckle noise, impulse noise, quantization noise, and feature dropout are applied to classical features prior to quantum encoding using a ZZ feature map. In parallel, hardware-inspired quantum noise channels such as depolarizing noise, amplitude damping, phase damping, Pauli errors, and readout errors are incorporated at the circuit level using the Qiskit Aer simulator. The experimental results indicate that noise in classical input data can significantly intensify the effects of quantum decoherence, resulting in less stable training and noticeably lower classification accuracy. Together, these observations emphasize the importance of designing and evaluating quantum machine learning pipelines with noise in mind, and highlight the need to consider classical and quantum noise simultaneously when assessing QML performance in the NISQ era
In current noisy intermediate-scale quantum (NISQ) devices, hybrid quantum neural networks (HQNNs) offer a promising solution, combining the strengths of classical machine learning with quantum computing capabilities. However, the performance of these networks can be significantly affected by the quantum noise inherent in NISQ devices. In this paper, we conduct an extensive comparative analysis of various HQNN algorithms, namely Quantum Convolution Neural Network (QCNN), Quanvolutional Neural Network (QuanNN), and Quantum Transfer Learning (QTL), for image classification tasks. We evaluate the performance of each algorithm across quantum circuits with different entangling structures, variations in layer count, and optimal placement in the architecture. Subsequently, we select the highest-performing architectures and assess their robustness against noise influence by introducing quantum gate noise through Phase Flip, Bit Flip, Phase Damping, Amplitude Damping, and the Depolarization Channel. Our results reveal that the top-performing models exhibit varying resilience to different noise channels. However, in most scenarios, the QuanNN demonstrates greater robustness across various quantum noise channels, consistently outperforming other models. This highlights the importance of tailoring model selection to specific noise environments in NISQ devices.
In current noisy intermediate-scale quantum (NISQ) devices, hybrid quantum neural networks (HQNNs) offer a promising solution, combining the strengths of classical machine learning with quantum computing capabilities. However, the performance of these networks can be significantly affected by the quantum noise inherent in NISQ devices. In this paper, we conduct an extensive comparative analysis of various HQNN algorithms, namely Quantum Convolution Neural Network (QCNN), Quanvolutional Neural Network (QuanNN), and Quantum Transfer Learning (QTL), for image classification tasks. We evaluate the performance of each algorithm across quantum circuits with different entangling structures, variations in layer count, and optimal placement in the architecture. Subsequently, we select the highest-performing architectures and assess their robustness against noise influence by introducing quantum gate noise through Phase Flip, Bit Flip, Phase Damping, Amplitude Damping, and the Depolarizing Channel. Our results reveal that the top-performing models exhibit varying resilience to different noise gates. However, in most scenarios, the QuanNN demonstrates greater robustness across various quantum noise channels, consistently outperforming other models. This highlights the importance of tailoring model selection to specific noise environments in NISQ devices.
Hybrid quantum-classical computing offers a promising pathway to harness quantum advantages while mitigating the hardware limitations of noisy intermediate-scale quantum (NISQ) devices. This work proposes a modular hybrid architecture that combines classical preprocessing, quantum-enhanced feature mapping through parameterized quantum circuits (PQCs), and adaptive classical optimization. The framework is designed to improve robustness and generalization of machine learning models in noisy environments, addressing critical issues such as overfitting, noise sensitivity, and hardware-induced errors. Experimental evaluations were conducted on benchmark datasets, including Iris, reduced-dimension MNIST, synthetic noisy XOR, Wine Quality, and Breast Cancer Wisconsin. The proposed model was tested under multiple noise models, such as Gaussian, salt-and-pepper, Poisson, and quantum-specific errors (bit-flip, phase-flip, and depolarizing), using 5-fold cross-validation and repeated trials for statistical rigor. Key performance metrics included Classification Accuracy (CA), Robustness Index (RI), and Training Stability (TS), with results reported as mean ± standard deviation and supported by paired t-tests (p < 0.05). Results demonstrate that the hybrid model consistently outperforms classical support vector machines (SVM) and standalone variational quantum classifiers (VQC) in noisy conditions, achieving smaller accuracy degradation and higher stability. The combination of quantum- enhanced feature mapping, optimizer synergy, and modular design is shown to be central to its resilience.
Quantum Machine Learning (QML) is an exciting frontier applying the philosophy of quantum computation to classical machine learning for improved learning performance of complicated tasks. However, QML is greatly challenged by realistic issues like noisy quantum measurements and limited labeled quantum data caused by the high cost and technological limitations. This paper explores the novel transfer-learning mechanisms to tackle these issues, which allow the reuse of the knowledge from related quantum tasks for improving model performance and generalization in resource-limited scenarios. Transfer learning, one of the great and successful concepts of classical machine learning, is to transfer the learned parameters or features from the source domain (or task) to the target domain (or task). We further develop and investigate, in quantum, a methodology where parameterized quantum circuits (PQCs) learnt on a source quantum data set have certain parts partially re-used or fine-tuned for a target task, given limited amount of target data. This strategy also avoids large quantum training runs that are not scalable on state-of-the-art noisy intermediate-scale quantum (NISQ) processors. The suggested methods are layerwise quantum adaptation, quantum kernel reuse and hybrid quantum-classical feature transfer. We investigate the robustness of these mechanisms in presence of noise by simulating quantum circuits using depolarizing and readout noise models. Moreover, we show that pretraining of quantum models on synthetic (or simulated) data sets (which are generated using larger, less noisy systems) can be instrumental in learning on real, small-sized quantum devices. Experiment results in quantum classification tasks demonstrate the effectiveness of transfer learning in boosting the accuracy, accelerating the convergence, and lowering the quantum circuit depth of successfully trained model. It is interesting to mention that transfer using layer-wise models became very resistant to quantum noise, as its performance did not degrade significantly for noisy inputs. These results demonstrate that transfer learning can overcome the scarce quantum data and suppress the harmful impact of measurement noise. This work presents a potential venue for enhancing the applicability of QML in the NISQ era. By transferring knowledge from theoretical quantum learning models to actual quantum hardware constraints, transfer learning allows quantum algorithms to learn faster and generalize better across tasks.