A quantum extension of SVM-perf for training nonlinear SVMs in almost linear time

Jonathan Allcock and Chang-Yu Hsieh

Tencent Quantum Laboratory

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We propose a quantum algorithm for training nonlinear support vector machines (SVM) for feature space learning where classical input data is encoded in the amplitudes of quantum states. Based on the classical SVM-perf algorithm of Joachims [1], our algorithm has a running time which scales linearly in the number of training examples $m$ (up to polylogarithmic factors) and applies to the standard soft-margin $\ell_1$-SVM model. In contrast, while classical SVM-perf has demonstrated impressive performance on both linear and nonlinear SVMs, its efficiency is guaranteed only in certain cases: it achieves linear $m$ scaling only for linear SVMs, where classification is performed in the original input data space, or for the special cases of low-rank or shift-invariant kernels. Similarly, previously proposed quantum algorithms either have super-linear scaling in $m$, or else apply to different SVM models such as the hard-margin or least squares $\ell_2$-SVM which lack certain desirable properties of the soft-margin $\ell_1$-SVM model. We classically simulate our algorithm and give evidence that it can perform well in practice, and not only for asymptotically large data sets.

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Cited by

[1] Seyran Saeedi, Aliakbar Panahi, and Tom Arodz, "Quantum semi-supervised kernel learning", Quantum Machine Intelligence 3 2, 24 (2021).

[2] Iordanis Kerenidis, Anupam Prakash, and Dániel Szilágyi, "Quantum algorithms for Second-Order Cone Programming and Support Vector Machines", Quantum 5, 427 (2021).

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