Choosing which features to retain from a high-dimensional dataset is one of the more practically consequential decisions in building a machine learning pipeline, yet it is rarely treated as anything other than a preprocessing afterthought. In this paper we ask whether quantum computation can play a meaningful role in that decision. We present Variational Quantum Feature Selection (VQFS), a method that assigns trainable scalar weights to input features and folds those weights directly into the rotation angles of a parameterized quantum circuit. An Ll penalty on the weight vector encourages many weights to collapse toward zero during training, leaving a compact subset of features that the circuit has learned to rely on. Because the weights are differentiable, the whole system-feature selector and quantum classifier together-is trained end-to-end with gradient descent rather than through the combinatorial search that makes classical wrapper methods expensive. We built VQFS on top of the PennyLane simulator so that it runs on an ordinary laptop without access to quantum hardware. Testing on the Breast Cancer Wisconsin, Wine, and Iris benchmarks, we found that VQFS matched or beat five classical baselines (Lasso, PCA, Random Forest importance, Mutual Information, and RFE) on accuracy while cutting the feature count by 25-60%. Five-fold cross-validation showed the gains were consistent rather than the result of a lucky split. We see this work as a small but concrete step toward integrating quantum methods into the full machine learning workflow, not just the classification stage.
Choosing which features to retain from a high-dimensional dataset is one of the more practically consequential decisions in building a machine learning pipeline, yet it is rarely treated as anything other than a preprocessing afterthought. In this paper we ask whether quantum computation can play a meaningful role in that decision. We present Variational Quantum Feature Selection (VQFS), a method that assigns trainable scalar weights to input features and folds those weights directly into the rotation angles of a parameterized quantum circuit. An Ll penalty on the weight vector encourages many weights to collapse toward zero during training, leaving a compact subset of features that the circuit has learned to rely on. Because the weights are differentiable, the whole system-feature selector and quantum classifier together-is trained end-to-end with gradient descent rather than through the combinatorial search that makes classical wrapper methods expensive. We built VQFS on top of the PennyLane simulator so that it runs on an ordinary laptop without access to quantum hardware. Testing on the Breast Cancer Wisconsin, Wine, and Iris benchmarks, we found that VQFS matched or beat five classical baselines (Lasso, PCA, Random Forest importance, Mutual Information, and RFE) on accuracy while cutting the feature count by 25-60%. Five-fold cross-validation showed the gains were consistent rather than the result of a lucky split. We see this work as a small but concrete step toward integrating quantum methods into the full machine learning workflow, not just the classification stage.
Choosing which features to retain from a high-dimensional dataset is one of the more practically consequential decisions in building a machine learning pipeline, yet it is rarely treated as anything other than a preprocessing afterthought. In this paper we ask whether quantum computation can play a meaningful role in that decision. We present Variational Quantum Feature Selection (VQFS), a method that assigns trainable scalar weights to input features and folds those weights directly into the rotation angles of a parameterized quantum circuit. An Ll penalty on the weight vector encourages many weights to collapse toward zero during training, leaving a compact subset of features that the circuit has learned to rely on. Because the weights are differentiable, the whole system-feature selector and quantum classifier together-is trained end-to-end with gradient descent rather than through the combinatorial search that makes classical wrapper methods expensive. We built VQFS on top of the PennyLane simulator so that it runs on an ordinary laptop without access to quantum hardware. Testing on the Breast Cancer Wisconsin, Wine, and Iris benchmarks, we found that VQFS matched or beat five classical baselines (Lasso, PCA, Random Forest importance, Mutual Information, and RFE) on accuracy while cutting the feature count by 25-60%. Five-fold cross-validation showed the gains were consistent rather than the result of a lucky split. We see this work as a small but concrete step toward integrating quantum methods into the full machine learning workflow, not just the classification stage.
Accurate and timely diagnosis of breast cancer greatly improves treatment effectiveness and patient survival. In this work, we have suggested a quantum-classical framework for breast cancer classification. The pipeline starts with Principal Component Analysis (PCA) to preserve 90-95% of the dataset variance. Then feature selection is done with Random Forest and Recursive Feature Elimination, retaining the top 10 strongest features. The features are normalized and fed to a Variational Quantum Circuit (VQC) Classifier built with the Pennylane library. The VQC employs a parameterized quantum circuit (PQC) with multiple rotation gates and entanglement layers and is run over a simulator. The experimental results demonstrate that the VQC attained a classification test accuracy of 96.49% and AUC-ROC of 0.9985 over the test set. The model has produced robust generalization performance, shown by its excellent performance over unknown data. Results indicate that the VQC competes with the benchmarked classical models and has potential benefits in terms of applicability and scalability to healthcare applications in terms of accuracy. This work emphasizes the synergy of classical preprocessing and quantum classification in designing efficient and interpretable healthcare applications.
In this study, the performance of classical and quantum machine learning models was compared using the Breast Cancer dataset, which consists of diagnostic data aimed at classifying breast tumor types. Breast cancer, being one of the most common and life-threatening cancers in women, requires accurate diagnostic tools for early detection and effective treatment. The primary objective of this study is to evaluate the accuracy of quantum-assisted models through quantum feature selection methods. Initially, classical machine learning algorithms such as Support Vector Machines (SVM), Decision Trees, Random Forests, and Logistic Regression were applied to the dataset for baseline analysis. Subsequently, a quantum feature map was constructed using the Cirq library, enabling feature transformation based on this map. The classification was performed using the SVM model with the quantum-transformed features. The Logistic Regression and SVM models demonstrated the highest performance among classical machine learning models, achieving an accuracy rate of 96.49%, followed by Random Forest at 94.74% and Decision Tree at 92.11%. In the context of quantum feature transformation, the model utilizing the top five selected features achieved an accuracy rate of 94.74%, in contrast to 98.25% for the model trained with all features. These findings underscore the potential of quantum feature maps in enhancing model performance compared to classical techniques. The results suggest that quantum computing may offer significant advantages when integrated into machine learning frameworks, particularly in domains such as medical diagnostics, where high accuracy is crucial.
Variational quantum classifiers (VQCs) are often compared with classical models under unequal data partitions, tuning budgets, or statistical treatment. We benchmark a 4-qubit VQC against logistic regression, an RBF-SVM, random forest, a quantum-kernel SVM (QSVM), and a majority-class control on Breast Cancer Wisconsin (n=569), Coimbra (n=116), and Golub leukemia (n=72), using five stratified folds across five seeds (25 paired blocks per dataset), identical training-fold-fitted preprocessing, and Nadeau–Bengio corrected paired tests with Holm correction. A first arm with a frozen configuration and the common [0, π] angle encoding left the VQC at 0.563, 0.515, and 0.576 accuracy (majority 0.617, 0.552, 0.652). In a second arm, in which every model was tuned by the same inner cross-validation and the encoding scale was a tuned hyperparameter, VQC accuracy rose to 0.864, 0.616, and 0.747. On Wisconsin the VQC stayed significantly below every matched classical model (differences 0.079–0.097, Holm p≤0.006) but above the majority class; on Golub it was significantly below only the SVM (Holm p=0.016); on Coimbra no comparison was significant. A QSVM with the same feature map exceeded the VQC numerically on every dataset, and three optimizers reached the same training loss in pilot diagnostics, pointing to the encoding and the variational head rather than the optimizer. The Wisconsin arms also differ in sample size (120 versus 569). Results apply to this low-qubit, PCA-compressed, exactly simulated setting and do not indicate a quantum advantage.
In recent years, quantum machine learning (QML) has emerged as a promising approach for medical decision-making, with representative methods such as Quantum Support Vector Machines (QSVMs), Quantum Neural Networks (QNNs), and Variational Quantum Circuits (VQCs). Among these, VQCs are distinguished by their expressive capability; however, their training on small biomedical datasets often results in instability and high variance. To address this issue, we propose a heterogeneous ensemble of six shallow VQCs evaluated on the Wisconsin Breast Cancer Diagnostic (WBCD) dataset, which contains 569 diagnostic samples characterized by 30 cytological features and binary class labels differentiating benign and malignant cases. The ensemble introduces diversity through distinct feature maps, circuit architectures, and optimization strategies, using rotation gates (RX, RY, RZ) combined with entangling layers (CNOT, CZ). Model reliability is estimated from out-of-bag errors, with normalized weights calculated as the inverse of the mean squared error, and final decisions obtained via weighted soft voting. The preprocessing pipeline involves median imputation and standardization, followed by principal component analysis (PCA) to compress the 30-dimensional space into three to four principal components for quantum encoding. In our experiments, individual VQCs trained on the full dataset exhibited unstable accuracies ranging from 80% to 96% across three–four qubits, whereas the proposed ensemble achieved consistent performance of 95.71% (3 qubits) and 96.43% (4 qubits), reducing variance and matching strong classical baselines such as SVM and Random Forest. All models were implemented in PennyLane, utilizing the Lightning.qubit simulator for shot-free optimization and the default.mixed device for noise-aware evaluation, demonstrating the potential of weighted quantum ensembles for reliable biomedical classification.
Feature selection in high-dimensional data spaces continues to be an intrinsic challenge in machine learning algorithms, especially when the number of features regularly exceeds thousands. Traditional techniques including mutual information filters, LASSO regularization, and recursive feature elimination are limited by the computational inefficiency of brute force searching as well as poor scalability in the ultra-high-dimensional setting. In this paper, a novel technique, the Hybrid Quantum-Classical Variational Algorithm (HQCVA), that combines quantum computing and classical optimization methods in the Variational Quantum Eigensolver (VQE) approach is introduced. Specifically, the quantum part is responsible for representing the correlation structure of the input features via the Ising model. To reduce composite loss, the classical sub-system uses the Adam stochastic optimizer to update variational parameters iteratively while also balancing density between predictive accuracies, densifications and depth of quantum circuits. Using five benchmark datasets (22,000 gene data; 12,000 pixels of hyperspectral imagery; 8,500 data points of financial time series), HQCVA produces a 12-18% higher F1-score than leading, state-of-the-art classical systems while decreasing the number of selected features by 40-65%. All simulations of circuits are performed on IBM Qiskit Aer with noise added, to simulate Eagle r3 consoles. Furthermore, the results indicate that the quantum advantage scales with increasing feature dimension through this process to demonstrate that near-term quantum devices have practical applications in real-world feature selection applications.