On the Impact of Weight Discretization in QUBO-Based SVM Training
This work addresses the problem of efficient SVM training for quantum computing applications, but it is incremental as it focuses on optimizing existing QUBO formulations.
The study investigated how weight discretization in QUBO-based SVM training affects predictive performance, finding that low-precision encodings (e.g., 1 bit per parameter) yield competitive or superior accuracy compared to classical LIBSVM, with increased bit-depth not always improving classification.
Training Support Vector Machines (SVMs) can be formulated as a QUBO problem, enabling the use of quantum annealing for model optimization. In this work, we study how the number of qubits - linked to the discretization level of dual weights - affects predictive performance across datasets. We compare QUBO-based SVM training to the classical LIBSVM solver and find that even low-precision QUBO encodings (e.g., 1 bit per parameter) yield competitive, and sometimes superior, accuracy. While increased bit-depth enables larger regularization parameters, it does not always improve classification. Our findings suggest that selecting the right support vectors may matter more than their precise weighting. Although current hardware limits the size of solvable QUBOs, our results highlight the potential of quantum annealing for efficient SVM training as quantum devices scale.