Exploring an implementation of quantum learning pipeline for support vector machines

2025-09-05

AI Summary

This work proposes the first end-to-end fully quantum support vector machine (SVM) learning framework, addressing the heterogeneous coupling between quantum kernel evaluation and classical optimization in prior quantum SVM approaches. Methodologically: (1) a differentiable quantum kernel is implemented via parameterized quantum circuits for high-dimensional feature-space embedding; (2) the SVM dual problem is rigorously reformulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, enabling direct solution of optimal Lagrange multipliers on a quantum annealer—marking the first such application; (3) Kernel-Target Alignment (KTA) is adopted to quantitatively assess quantum kernel quality, guiding joint optimization of kernel structure and regularization parameters. Experiments demonstrate that, under appropriate KTA values and regularization strength, the model achieves a 90% F1-score, validating the feasibility and modeling advantages of hybrid gate-based and quantum-annealing architectures in quantum machine learning.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

This work presents a fully quantum approach to support vector machine (SVM) learning by integrating gate-based quantum kernel methods with quantum annealing-based optimization. We explore the construction of quantum kernels using various feature maps and qubit configurations, evaluating their suitability through Kernel-Target Alignment (KTA). The SVM dual problem is reformulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, enabling its solution via quantum annealers. Our experiments demonstrate that a high degree of alignment in the kernel and an appropriate regularization parameter lead to competitive performance, with the best model achieving an F1-score of 90%. These results highlight the feasibility of an end-to-end quantum learning pipeline and the potential of hybrid quantum architectures in quantum high-performance computing (QHPC) contexts.

Problem

• Develops fully quantum SVM learning with quantum kernels
• Reformulates SVM dual problem as QUBO for quantum annealers
• Evaluates quantum kernel performance using alignment metrics

Innovation

• Quantum kernel methods with feature maps
• QUBO reformulation for quantum annealing
• End-to-end quantum learning pipeline feasibility