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This book is organized by [TheQuantHackers Community] (https://llmquant.com/) and provides PDF downloads. It is only for learning and communication. The copyright belongs to the original author.
This book is organized by [TheQuantHackers Community] () and provides PDF downloads. It is only for learning and communication. The copyright belongs to the original author.
<img src="1.png" alt="Quantum Machine Learning and Optimization in Finance- On the Road to Quantum Advantage" width="200"/> <!-- @@@ --> <!-- @@@ --> <!-- @@@ --> <!-- @@@ --> <!-- @@@ --> <!-- @@@ --> <!-- @@@ --> <!-- @@@ --> <img src="1.png" alt="Quantum Machine Learning and Optimisation in Finance- On the Road to Quantum Advantage" width="200"/> <img src="1.png" alt="Quantum Machine Learning and Optimisation in Finance- On the Road to Quantum Advantage" width="200"/>Quantum Machine Learning and Optimization in Finance - On the Road to Quantum Advantage is a professional book on quantitative finance, covering the application of quantum machine learning and optimization in the financial field. This book provides an in-depth look at how Noisy Intermediate Scale Quantum (NISQ) hardware can be leveraged to create hybrid quantum-classical machine learning and optimization models with the goal of achieving quantum advantage in financial applications. The book details various major quantum computing algorithms that can be applied to existing NISQ equipment and highlights the practical application potential of the quantum computing paradigm in the financial field.
The content of this book is divided into two parts: The first part focuses on simulated quantum computing, especially quantum annealing, covering topics such as adiabatic quantum computing, quadratic unconstrained binary optimization (QUBO), quantum boosting and quantum Boltzmann machines. The second part focuses on gate model quantum computing, including qubits and quantum logic gates, parameterized quantum circuits and data encoding, quantum neural networks, quantum circuit Boltzmann machines, variational quantum eigensolvers (VQE), and quantum approximate optimization algorithms (QAOA), etc. This book is designed to help readers grasp the principles of quantum machine learning and apply them to solve complex optimization problems in finance, such as training parameterized quantum circuits as generative models and utilizing quantum boosting techniques.
Here is a preview of the book’s main chapters:
本书籍由TheQuantHackers社区整理, 并提供PDF下载, 只供学习交流使用, 版权归原作者所有。Quantum Machine Learning and Optimisation in Finance- On the Road to Quantum Advantage 是一本关于量化金融的专业书籍，涵盖了量子机器学习和优化在金融领域的应用。本书深入探讨了如何利用噪声中等规模量子（NISQ）硬件创建混合量子-经典机器学习和优化模型，以期在金融应用中实现量子优势。书中详细介绍了可应用于现有NISQ设备的各种主要量子计算算法，并强调了量子计算范式在金融领域的实际应用潜力。
本书内容分为两大部分：第一部分聚焦于模拟量子计算，特别是量子退火，涵盖了绝热量子计算、二次无约束二元优化（QUBO）、量子提升和量子玻尔兹曼机等主题。第二部分则侧重于门模型量子计算，包括量子比特与量子逻辑门、参数化量子电路与数据编码、量子神经网络、量子电路玻尔兹曼机、变分量子本征求解器（VQE）以及量子近似优化算法（QAOA）等。本书旨在帮助读者掌握量子机器学习的原理，并将其应用于解决金融中的复杂优化问题，例如训练参数化量子电路作为生成模型，以及利用量子提升技术。
以下是本书的主要章节预览：
本书籍由TheQuantHackers社区整理, 并提供PDF下载, 只供学习交流使用, 版权归原作者所有。
Quantum Machine Learning and Optimisation in Finance- On the Road to Quantum Advantage 是一本关于量化金融的专业书籍，涵盖了量子机器学习和优化在金融领域的应用。本书深入探讨了如何利用噪声中等规模量子（NISQ）硬件创建混合量子-经典机器学习和优化模型，以期在金融应用中实现量子优势。书中详细介绍了可应用于现有NISQ设备的各种主要量子计算算法，并强调了量子计算范式在金融领域的实际应用潜力。
本书内容分为两大部分：第一部分聚焦于模拟量子计算，特别是量子退火，涵盖了绝热量子计算、二次无约束二元优化（QUBO）、量子提升和量子玻尔兹曼机等主题。第二部分则侧重于门模型量子计算，包括量子比特与量子逻辑门、参数化量子电路与数据编码、量子神经网络、量子电路玻尔兹曼机、变分量子本征求解器（VQE）以及量子近似优化算法（QAOA）等。本书旨在帮助读者掌握量子机器学习的原理，并将其应用于解决金融中的复杂优化问题，例如训练参数化量子电路作为生成模型，以及利用量子提升技术。
以下是本书的主要章节预览：
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