product iamge

Improving ML Algorithmic Time Complexity Using Quantum Infrastructure

This document was uploaded by one of our users. The uploader already confirmed that they had the permission to publish it. If you are author/publisher or own the copyright of this documents, please report to us by using this DMCA report form.

Download
Search on Amazon

Simply click on the Download Book button.

Yes, Book downloads on Ebookily are 100% Free.

Sometimes the book is free on Amazon As well, so go ahead and hit "Search on Amazon"

This paper questions current classical machine learning practices by comparing them to their quantum alternatives and addressing the applications and limitations of this new approach.

Author(s): Aayush Grover
Edition: 1
Year: 2023

Language: English
Pages: 14
City: Ottawa
Tags: Machine Learning, Quantum Physics, Computer Science

Abstract
Introduction to Machine Learning and Quantum Computing
Moore’s Law and State of the Industry
Quantum Computing and Qubits
Quantum Advantage for Algorithms: Why Quantum?
Algorithmic Time Complexity
Relevance to Machine Learning
Big O Notation
Quantum Machine Learning (QML)
Solving Linear Systems Classically
The HHL Algorithm - A Quantum Alternative
Quantum Approach to SVMs
Mathematically Modeling the SVM
The Gap in Classical SVMs
Quantum SVMs (QSVMs)
Encoding Classical Data for Quantum Machine Learning
Basis Encoding
Amplitude Encoding
Conclusion
References