Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Arrow up icon
GO TO TOP
Quantum Machine Learning and Optimisation in Finance

You're reading from   Quantum Machine Learning and Optimisation in Finance Drive financial innovation with quantum-powered algorithms and optimisation strategies

Arrow left icon
Product type Paperback
Published in Dec 2024
Publisher Packt
ISBN-13 9781836209614
Length 494 pages
Edition 2nd Edition
Languages
Arrow right icon
Authors (2):
Arrow left icon
Jacquier Antoine Jacquier Antoine
Author Profile Icon Jacquier Antoine
Jacquier Antoine
Alexei Kondratyev Alexei Kondratyev
Author Profile Icon Alexei Kondratyev
Alexei Kondratyev
Arrow right icon
View More author details
Toc

Table of Contents (21) Chapters Close

Preface 1. Chapter 1 The Principles of Quantum Mechanics 2. Part I Analog Quantum Computing – Quantum Annealing FREE CHAPTER
3. Chapter 2 Adiabatic Quantum Computing 4. Chapter 3 Quadratic Unconstrained Binary Optimisation 5. Chapter 4 Quantum Boosting 6. Chapter 5 Quantum Boltzmann Machine 7. Part II Gate Model Quantum Computing
8. Chapter 6 Qubits and Quantum Logic Gates 9. Chapter 7 Parameterised Quantum Circuits and Data Encoding 10. Chapter 8 Quantum Neural Network 11. Chapter 9 Quantum Circuit Born Machine 12. Chapter 10 Variational Quantum Eigensolver 13. Chapter 11 Quantum Approximate Optimisation Algorithm 14. Chapter 12 Quantum Kernels and Quantum Two-Sample Test 15. Chapter 13 The Power of Parameterised Quantum Circuits 16. Chapter 14 Advanced QML Models 17. Chapter 15 Beyond NISQ 18. Bibliography
19. Index 20. Other Books You Might Enjoy

12.3 Quantum Circuits for Feature Maps

Figure 12.1 displays a schematic representation of the feature map circuit. In this example, we work with an 8-dimensional dataset with features encoded in the rotation angles such that there is a direct mapping of a sample xi := (x1i,…,x8i) into the vector of adjustable circuit parameters 𝜃i := (𝜃1i,…,𝜃8i). The first section of the circuit implements the operator U(xi), creating an entangled state due to the layer of fixed two-qubit CZ gates, whereas the second section of the circuit implements U(xj). Here, we use the fact that

R†X(𝜃) = RX(− 𝜃), R†Y(𝜃) = RY(− 𝜃), CZ† = CZ.

It is easy to see that if the samples xi and xj are identical (so that 𝜃i = 𝜃j), then U(xi)U(xj) = I and all K measurements will return the all-zero string |0 ⟩.

 †iiiiiiiiijjjjjjjjj RRRRRRRRRRRRRR|0|0|0|0YYYYXXXXXXXXYY⟩⟩⟩⟩((((((((((((((𝜃1𝜃2𝜃3𝜃4𝜃5𝜃6𝜃7𝜃8−−−−−−))))))))𝜃𝜃𝜃𝜃𝜃&#x56781)))))

Figure 12.1: Schematic quantum kernel circuit.

The rest of the protocol is classical – the quantum computer is used to assist the classifier with the...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime