Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quantum Sparse Support Vector Machines   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 a $\Omega(m)$ worst-case lower bound for
computational complexity of any quantum training algorithm relying on black-box
access to training samples; quantum sparse SVM has at least linear worst-case
complexity. However, we prove that there are realistic scenarios in which a
sparse linear classifier is expected to have high accuracy, and can be trained
in sublinear time in terms of both the number of training samples and the
number of features.

21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an