Quick Navigation

Overview Topics Abstract Importance Paper Tools References Reactions Discussion Publication Related

Topics

Quantum Machine Learning

Provable advantages of kernel-based quantum learners and quantum preprocessing based on Grover's algorithm

arXiv

Authors: Till Muser, Elias Zapusek, Vasilis Belis, Florentin Reiter

Year

2023

Paper ID

54461

Status

Preprint

Abstract Read

~2 min

Abstract Words

102

Citations

N/A

Abstract

There is an ongoing effort to find quantum speedups for learning problems. Recently, \[Y. Liu et al., Nat. Phys. textbf{17}, 1013--1017 (2021)\] have proven an exponential speedup for quantum support vector machines by leveraging the speedup of Shor's algorithm. We expand upon this result and identify a speedup utilizing Grover's algorithm in the kernel of a support vector machine. To show the practicality of the kernel structure we apply it to a problem related to pattern matching, providing a practical yet provable advantage. Moreover, we show that combining quantum computation in a preprocessing step with classical methods for classification further improves classifier performance.

Why This Paper Matters

This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2023 reference point for readers tracking recent quantum research.
There is an ongoing effort to find quantum speedups for learning problems.

Paper Tools

Become a member to use research tools

Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.

Become a member Sign in

Show Paper arXiv Publisher Share Cite This Paper Copy URL Compare Copy DOI Add to Reading List Category Correction Request

References & Citation Signals

[1] DOI https://doi.org/arXiv:2309.14406 [2] arXiv https://arxiv.org/abs/2309.14406 [3] Publisher https://arxiv.org/abs/2309.14406

Local Citation Graph (Related-Paper Links)

External citation index: OpenAlex citation signal

Community Reactions

Quick sentiment from readers on this paper.

Score: 0

Likes: 0 Dislikes: 0

Discussion & Reviews (Moderated)

Average Rating: 0.0 / 5 (0 ratings)

No written reviews yet.