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Quantum Machine Learning and Optimisation in Finance

You're reading from   Quantum Machine Learning and Optimisation in Finance On the Road to Quantum Advantage

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781801813570
Length 442 pages
Edition 1st Edition
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Authors (2):
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Alexei Kondratyev Alexei Kondratyev
Author Profile Icon Alexei Kondratyev
Alexei Kondratyev
Jacquier Antoine Jacquier Antoine
Author Profile Icon Jacquier Antoine
Jacquier Antoine
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Toc

Summary

In this chapter, we learned the key principles of quantum mechanics, starting with a review of the basic elements of linear algebra, followed by an introduction to Dirac notations.

We then covered the main postulates of quantum mechanics and their relevance to quantum computing. We learned how to describe the state (statics) and the evolution (dynamics) of a closed system, the interactions of a system with external systems (measurement), observables, as well as the state of a composite system in terms of its component parts.

We finally introduced the density operator, which allows us to describe both pure and mixed quantum states, contrasting with the state vector, which can only represent pure quantum states.

In the next chapter, we will look at an application of the principles of quantum mechanics to analog quantum computing – quantum annealing.

You have been reading a chapter from
Quantum Machine Learning and Optimisation in Finance
Published in: Oct 2022
Publisher: Packt
ISBN-13: 9781801813570
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