Modeling Deep Unfolded Quantum Machine Learning Framework

Quantum machine learning models, like Quantum Neural Networks (QNN) and Quantum Support Vector Classifiers (QSVC), often struggle with overfitting, slow convergence, and suboptimal generalization across various datasets. This paper explores the advantages of integrating deep unfolding techniques into quantum models and develops a framework focusing on Deep Unfolded Variational Quantum Classifiers (DVQC), Deep Unfolded Quantum Neural Networks (DQNN), and Deep Unfolded QSVC (DQSVC). Our novel unfolding transforms quantum circuit training into a sequence of learnable layers, with each layer representing an optimization step that concurrently renews both circuit parameters and QNN hyperparameters. The proposed framework significantly improves training and test accuracy by dynamically adjusting learning rate, perturbations and other similar hyperparameters, particularly on complex datasets like Genomic and Breast Cancer. Our evaluation and experiment show that proposed DVQC and DQNN outperform baseline VQC and QNN, achieving 90% training accuracy and up to 20% higher test accuracy on Genomic and Adhoc datasets. DQSVC achieves 100% accuracy on Adhoc and 97% on Genomic datasets, surpassing the 90% test accuracy of traditional QSVC. Our implementation details will be publicly available.