Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quantum Semi-Supervised Learning with Quantum Supremacy   Quantum machine learning promises to efficiently solve important problems.
There are two persistent challenges in classical machine learning: the lack of
labeled data, and the limit of computational power. We propose a novel
framework that resolves both issues: quantum semi-supervised learning.
Moreover, we provide a protocol in systematically designing quantum machine
learning algorithms with quantum supremacy, which can be extended beyond
quantum semi-supervised learning. In the meantime, we show that naive quantum
matrix product estimation algorithm outperforms the best known classical matrix
multiplication algorithm. We showcase two concrete quantum semi-supervised
learning algorithms: a quantum self-training algorithm named the propagating
nearest-neighbor classifier, and the quantum semi-supervised K-means clustering
algorithm. By doing time complexity analysis, we conclude that they indeed
possess quantum supremacy.

28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys