Improving ML Algorithmic Time Complexity Using Quantum Infrastructure 1st Edition by Aayush Grover ISBN 9780470525890 0470525894

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ISBN 10: 0470525894
ISBN 13: 9780470525890
Author: Aayush Grover

With the rising popularity of machine learning in the past decade, a stronger urgency has been placed on drastically improving computational technology. Despite recent advancements in this industry, the speed at which our technologies can complete machine learning tasks continues to be its most significant bottleneck. Modern machine learning algorithms are notorious for requiring a substantial amount of computational power. As the demand for computational power increases, so does the demand for new ways to improve the speed of these algorithms. Machine learning researchers have turned to leverage quantum computation to significantly improve their algorithms’ time complexities. This counteracts the physical limitations that come with the chips used in our technology today. 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.

Improving ML Algorithmic Time Complexity Using Quantum Infrastructure 1st Edition Table of contents:

1. Introduction to Machine Learning and Quantum Computing
    1.1 Moore’s Law and State of the Industry
    1.2 Quantum Computing and Qubits
    1.3 Quantum Advantage for Algorithms: Why Quantum?

2. Algorithmic Time Complexity
    2.1 Relevance to Machine Learning
    2.2 Big O Notation

3. Quantum Machine Learning (QML)
    3.1 Solving Linear Systems Classically
    3.2 The HHL Algorithm – A Quantum Alternative

4. Quantum Approach to SVMs
    4.1 Mathematically Modeling the SVM
    4.2 The Gap in Classical SVMs
    4.3 Quantum SVMs (QSVMs)

5. Encoding Classical Data for Quantum Machine Learning
    5.1 Basis Encoding
    5.2 Amplitude Encoding

6. Conclusion

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Tags: Aayush Grover, Improving ML Algorithmic, Quantum Infrastructure