Quantum Machine Learning and Optimisation in Finance: On the Road to Quantum Advantage (Paperback)

Learn the principles of quantum machine learning and how to apply them in finance.

Purchase of the print or Kindle book includes a free eBook in PDF format.

Key Features

• Discover how to solve optimisation problems on quantum computers that can provide a speedup edge over classical methods
• Use methods of analogue and digital quantum computing to build powerful generative models
• Create the latest algorithms that work on Noisy Intermediate-Scale Quantum (NISQ) computers

Book Description

With recent advances in quantum computing technology, we finally reached the era of Noisy Intermediate-Scale Quantum (NISQ) computing. NISQ-era quantum computers are powerful enough to test quantum computing algorithms and solve hard real-world problems faster than classical hardware.

Speedup is so important in financial applications, ranging from analysing huge amounts of customer data to high frequency trading. This is where quantum computing can give you the edge. Quantum Machine Learning and Optimisation in Finance shows you how to create hybrid quantum-classical machine learning and optimisation models that can harness the power of NISQ hardware.

This book will take you through the real-world productive applications of quantum computing. The book explores the main quantum computing algorithms implementable on existing NISQ devices and highlights a range of financial applications that can benefit from this new quantum computing paradigm.

This book will help you be one of the first in the finance industry to use quantum machine learning models to solve classically hard real-world problems. We may have moved past the point of quantum computing supremacy, but our quest for establishing quantum computing advantage has just begun!

What you will learn

• Train parameterised quantum circuits as generative models that excel on NISQ hardware
• Solve hard optimisation problems
• Apply quantum boosting to financial applications
• Learn how the variational quantum eigensolver and the quantum approximate optimisation algorithms work
• Analyse the latest algorithms from quantum kernels to quantum semidefinite programming
• Apply quantum neural networks to credit approvals

Who this book is for

This book is for Quants and developers, data scientists, researchers, and students in quantitative finance. Although the focus is on financial use cases, all the methods and techniques are transferable to other areas.

學習量子機器學習的原則，以及如何在金融領域應用它們。

購買印刷版或Kindle書籍，將包含一本免費的PDF電子書。

主要特點：

- 發現如何在量子計算機上解決優化問題，以提供比傳統方法更快的速度優勢。
- 使用類比和數字量子計算方法構建強大的生成模型。
- 創建適用於噪聲中等規模量子（NISQ）計算機的最新算法。

書籍描述：

隨著量子計算技術的最新進展，我們終於進入了噪聲中等規模量子（NISQ）計算的時代。NISQ時代的量子計算機足夠強大，可以測試量子計算算法並比傳統硬件更快地解決困難的現實世界問題。

在金融應用中，速度對於分析大量客戶數據到高頻交易都非常重要。這就是量子計算可以給你帶來優勢的地方。《金融中的量子機器學習和優化》向您展示如何創建混合量子-經典機器學習和優化模型，以利用NISQ硬件的威力。

本書將帶您深入了解量子計算的實際應用。該書探討了可在現有NISQ設備上實施的主要量子計算算法，並突出了一系列可以從這種新的量子計算範式中受益的金融應用。

本書將幫助您成為金融行業中首批使用量子機器學習模型解決傳統困難現實世界問題的人之一。我們可能已經超越了量子計算的至高點，但我們在建立量子計算優勢的探索才剛剛開始！

您將學到什麼：

- 在NISQ硬件上訓練參數化量子電路作為生成模型。
- 解決困難的優化問題。
- 將量子增強應用於金融領域。
- 了解變分量子本徵求解器和量子近似優化算法的工作原理。
- 分析從量子核函數到量子半定規劃的最新算法。
- 將量子神經網絡應用於信用審批。

本書適合對象：

本書適合量化分析師和開發人員、數據科學家、研究人員和量化金融領域的學生。儘管重點是金融應用案例，但所有的方法和技術都可以應用於其他領域。

1. The Principles of Quantum Mechanics
2. Adiabatic Quantum Computing
3. Quadratic Unconstrained Binary Optimisation
4. Quantum Boosting
5. Quantum Boltzmann Machine
6. Qubits and Quantum Logic Gates
7. Parameterised Quantum Circuits and Data Encoding
8. Quantum Neural Network
9. Quantum Circuit Born Machine
10. Variational Quantum Eigensolver
11. Quantum Approximate Optimisation Algorithm
12. The Power of Parameterised Quantum Circuits
13. Looking Ahead
14. Bibliography

量子力學原理
绝热量子计算
二次无约束二进制优化
量子增强
量子玻尔兹曼机
量子比特和量子逻辑门
参数化量子电路和数据编码
量子神经网络
量子电路波恩机
变分量子本征求解器
量子近似优化算法
参数化量子电路的威力
展望未来
参考文献