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We analyze the computational complexity of Quantum Sparse Support Vector Machine, a linear classifier that minimizes the hinge loss and the L_1 norm of the feature weights vector and relies on a quantum linear programming solver instead of a classical solver. Sparse SVM leads to sparse models that use only a small fraction of the input features in making decisions, and is especially useful when the total number of features, p, approaches or exceeds the number of training samples, m. We prove aarXiv:1902.01879v4 fatcat:phqhimzcdjfghgc2o4zz5a7kci