Formulation-Level Auto-Tuning for QUBO-Based Machine Learning: A Case Study Across Multiple Quantum-Inspired Annealers

תקציר מקורי באנגליתarXiv:2607.18774v1 Announce Type: new Abstract: This paper presents an Optuna-based formulation-level auto-tuning framework for support vector machines (SVMs) implemented on multiple quantum-inspired annealers. In an annealing-based SVM, continuous dual variables are discretized and converted into a quadratic unconstrained binary optimization (QUBO) model. This transformation introduces three coupled classes of parameters: representation parameters-the encoding base B and bit depth K-which determine numerical range, resolution, and QUBO size; the RBF kernel parameter {\gamma}, which determines classifier geometry; and the equality-constraint penalty {\xi}, which controls feasibility and coefficient balance. We formulate their joint selection as a mixed discrete-continuous black-box optimiz