Quantum Machine Learning: An Applied Approach by Santanu Ganguly

Author:Santanu Ganguly
Language: eng
Format: epub
ISBN: 9781484270981
Publisher: Apress

2Aspen, Forest, Forest SDK, Pyquil, QCS, Quantum Advantage Prize, Quil, Quilc, Quil-T, QVM, and Rigetti are trademarks or registered trademarks of Rigetti & Co, Inc.

© Santanu Ganguly 2021

S. GangulyQuantum Machine Learning: An Applied Approachhttps://doi.org/10.1007/978-1-4842-7098-1_6

6. QML Algorithms II

Santanu Ganguly1

(1)Ashford, UK

Let your life lightly dance on the edges of Time like dew on the tip of a leaf.

—Rabindranath Tagore

Quantum machine learning provides unprecedented scope in computing the techniques done in classical machine learning on a quantum computer. The entanglement and superposition of the basic qubit states promise to provide an edge over the performance and scope of classical machine learning.

By now, you should have some insights into how classical-quantum systems work across their interfaces. Currently, there are two promising ways to utilize quantum computers for machine learning tasks.The first way constructs a “quantum version” of the classical machine learning models so that the quantum algorithms produce results analogous to the said classical model.

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