低精度量子编码训练SVM仍可达到优秀性能，关键在选对支持向量。
On the Impact of Weight Discretization in QUBO-Based SVM Training
- 用量子退火求解QUBO形式的SVM，通过参数位数控制精度
- 1比特编码已可媲美甚至超过经典LIBSVM，且高精度未必更优
- 适合关注量子计算赋能传统机器学习的科研与工程人员
支持向量机（SVM）的训练可转化为QUBO问题，从而利用量子退火进行优化。本文研究了双权重离散化程度（关联到量子比特数量）对不同数据集上预测性能的影响。将基于QUBO的SVM训练与经典LIBSVM求解器对比发现，即使采用极低精度的编码（如每参数1比特），其准确率仍具竞争力，有时还更优。虽然增加位深允许更大的正则化参数，但并不总能提升分类效果。结果表明，选择合适的支持向量可能比精确赋权更重要。尽管当前硬件限制了可解QUBO规模，但本研究凸显了随着量子设备发展，量子退火在高效SVM训练中的潜力。
原文摘要 · Abstract (English)
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.
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