High-dimensional classification demands expressive feature representations, robust optimization, and manageable computational cost. This paper proposes a hybrid quantum– classical framework combining classical feature screening and compression, a shallow parameterized quantum circuit, measurement-derived quantum features, and a regularized classical classifier. The design explicitly addresses the mismatch between hundreds of original attributes and the small qubit counts available to near-term devices. Its training objective combines cross-entropy, complexity regularization, and a noise-sensitivity penalty, while alternating classical and quantum parameter updates. A reproducible evaluation protocol is specified using stratified data partitions, classical and quantum baselines, noise sweeps, and resource accounting. To demonstrate how the analysis should be reported, illustrative numerical results and plots are provided; they are not outcomes of executed quantum experiments and must be replaced by measured values before publication. The framework offers a testable route for evaluating whether compact quantum representations improve predictive performance or robustness relative to equally budgeted classical models, without presupposing quantum advantage.
High-dimensional classification demands expressive feature representations, robust optimization, and manageable computational cost. This paper proposes a hybrid quantum– classical framework combining classical feature screening and compression, a shallow parameterized quantum circuit, measurement-derived quantum features, and a regularized classical classifier. The design explicitly addresses the mismatch between hundreds of original attributes and the small qubit counts available to near-term devices. Its training objective combines cross-entropy, complexity regularization, and a noise-sensitivity penalty, while alternating classical and quantum parameter updates. A reproducible evaluation protocol is specified using stratified data partitions, classical and quantum baselines, noise sweeps, and resource accounting. To demonstrate how the analysis should be reported, illustrative numerical results and plots are provided; they are not outcomes of executed quantum experiments and must be replaced by measured values before publication. The framework offers a testable route for evaluating whether compact quantum representations improve predictive performance or robustness relative to equally budgeted classical models, without presupposing quantum advantage.
As the dimensionality and complexity of engineering data grows, traditional machine learning systems become increasinglydifficult to apply, especially when detecting informative features without sacrificing predictive accuracy and computationalefficiency. In this paper we proposed a Hybrid Quantum-Classical Learning Framework for Intelligent Feature Optimization andPredictive Analytics in Engineering Applications. The framework is a combination of classical data preprocessing, intelligentfeature selection, quantum-enhanced feature representation, parameterized quantum circuits and predictive learning in a unifiedoptimization pipeline. Engineering features are first normalized and evaluated to find informative subsets and minimizeredundancy. These selected features are then mapped onto the quantum feature space and parameterized quantum circuits tocreate improved representations for predictive modeling. The optimization objective takes into account predictive loss, size ofthe feature subset and regularization to achieve an efficient feature configuration. This quantum-classical representation is fed to the classical predictive models for classification and evaluation of the models. The framework is assessed based on predictiveaccuracy, ROC properties, feature reduction, convergence behavior, computational time, scalability and prediction error. Thepotential of using intelligent feature optimization combined with quantum enhanced representation for engineering analytics isillustrated by comparing it to conventional machine learning and hybrid learning models. This study provides a flexibleframework that enables the merging of quantum and classic machine learning, and shows how hybrid architectures can be usedin high-dimensional engineering prediction.
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 Neural Networks (QNNs) represent a compelling convergence of quantum mechanics and machine learning, yet their practical deployment remains constrained by noise, limited qubit counts, and the cost of full quantum execution. Hybrid quantum-classical (HQC) training offers a pragmatic path forward: parameterized quantum circuits (PQCs) process information in exponentially large Hilbert spaces while classical optimizers update parameters via gradient methods accessible on today's hardware. We present a modular HQC training framework, formalising the parameter-shift rule for hardware-compatible gradient estimation, characterising the barren-plateau phenomenon through a variance-decay analysis, and introducing noise-aware training strategies for NISQ devices. Empirical results from state-vector and shot-based simulation confirm measurable quantum advantage on quantum-structured tasks, with the phase classification benchmark improving +12.1% over a classical MLP baseline. The accompanying open-source library provides a clean abstraction between quantum simulation and classical optimisation, serving as a foundation for near-term quantum machine learning research.
Abstract This research presents a comprehensive hybrid quantum classical machine learning framework designed to overcome the computational limitations of classical algorithms when processing high dimensional data. The study integrates parameterized quantum circuits within classical neural network architectures and demonstrates their application across optimization tasks and supervised learning problems. The framework was evaluated on combinatorial optimization benchmarks including Max Cut and Job Shop Scheduling problems alongside classification tasks using quantum annealing based feature selection. Results demonstrate that hybrid quantum classical models achieve faster convergence rates and improved accuracy in high dimensional spaces compared to classical counterparts with the quantum algorithm for Max Cut optimization outperforming complete solution searches on larger problem instances. The findings provide machine learning practitioners and quantum computing researchers with validated hybrid architectures for near term quantum device deployment.
Abstract Hybrid quantum–classical learning has emerged as a promising paradigm for leveraging near-term quantum devices in practical machine learning tasks. However, many existing hybrid architectures rely on tightly coupled end-to-end training schemes that are difficult to optimize, sensitive to quantum noise, and strongly dependent on specific encoder designs. In this work, we introduce Quantum LEGO Learning, a modular framework for hybrid artificial intelligence that decomposes learning into reusable classical and quantum blocks with explicitly separated roles. Within this framework, a pre-trained classical neural network acts as a frozen feature extractor, while a variational quantum circuit (VQC) serves as the only trainable, adaptive module that operates on structured latent representations. This design enables stable optimization, reduces parameter coupling between classical and quantum components, and supports architecture-agnostic integration across different classical backbones. We develop a block-wise theoretical analysis that decomposes the learning behavior of the hybrid model into components of approximation, estimation, optimization, and noise-induced error. The analysis shows that once expressive classical representations are fixed, the overall model's approximation capability depends primarily on the classical feature block, while the trainable quantum module governs the complexity of downstream adaptation. We further derive optimization and noise-resilience bounds demonstrating how freezing the classical block suppresses the propagation of noisy gradients during training. To validate the proposed framework, we conduct systematic experiments on quantum dot classification and genome transcription factor binding site prediction using multiple frozen feature extractors, including ResNet and tensor-train network backbones, combined with both quantum and classical adaptive heads. Experimental results from noiseless simulations, realistic quantum-noise models, and IBM Heron quantum hardware demonstrate stable optimization, robustness to noise, and improved performance of VQC-based adaptive heads under matched parameter budgets. These results suggest that modular hybrid architectures provide a practical and scalable pathway for quantum-enhanced learning in the NISQ regime.
Large language and sentence-embedding models provide rich semantic representations, but their high dimensionality poses a challenge for near-term quantum machine learning (QML), where quantum circuits can process only a limited number of input features. We investigate a hybrid quantum-classical pipeline that transforms high-dimensional sentence embeddings into compact representations for variational quantum classification. The workflow combines a pretrained sentence-embedding model, dimensionality reduction, angle encoding, a variational quantum circuit (VQC), and a classical decision layer. We systematically compare principal component analysis (PCA), neighborhood components analysis (NCA), and linear discriminant analysis (LDA), covering both unsupervised and supervised dimensionality reduction. Using the TREC question-classification dataset, we study the relationship between representation dimensionality, information retention, qubit count, and classification performance. Preliminary PCA experiments reveal a strong information bottleneck: reducing 768-dimensional embeddings to 3, 4, 5, and 8 dimensions retains about 8.2%, 10.2%, 11.9%, and 16.4% of the variance, with corresponding classification accuracies of 50.3%, 51.2%, 57.9%, and 63.4%. In contrast, supervised reduction is substantially more efficient. LDA reaches 85.3% accuracy and NCA reaches 83.1% using only 5 dimensions, under a leakage-free cross-validation protocol, compared with 85.1% for a full 384-dimensional classical baseline. These results indicate that supervised dimensionality reduction can preserve task-relevant information far more effectively than variance-based compression, making compact representations a promising route toward practical hybrid quantum-classical NLP models.