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1. A Comprehensive Bridge Between Quantum Computing and Finance
   As someone deeply interested in the intersection of quantum computing and financial applications, I found “Quantum Machine Learning and Optimization in Finance” by Jacquier and Kondratyev to be an exceptionally well-structured and insightful resource. The book expertly navigates the complex terrain between quantum computing theory and practical financial applications without sacrificing mathematical rigor or accessibility.The book is organized into two main parts: analog quantum computing (focusing on quantum annealing) and gate model quantum computing. This structure allows readers to build their understanding progressively from fundamental concepts to complex applications. I particularly appreciated how the authors begin with essential quantum mechanics principles, making the material accessible to readers without a physics background.This book’s pragmatic approach to NISQ (Noisy Intermediate-Scale Quantum) era applications sets it apart. Rather than focusing on theoretical algorithms requiring fault-tolerant quantum computers, the authors emphasize techniques that can deliver value on current hardware. Their thorough coverage of parameterized quantum circuits, quantum neural networks, and quantum circuit-born machines provides actionable insights for those looking to experiment with quantum solutions today.The financial applications discussed are not merely academic exercises but address genuine industry challenges—portfolio optimization, credit scoring, market generation, and more. Including classical benchmarks throughout the book offers valuable context, helping readers understand where quantum approaches offer advantages over traditional methods.Why STEM students should read this book:If you’re a STEM student interested in quantum computing or quantitative finance, this book provides an excellent foundation in both fields. The authors carefully develop mathematical concepts from first principles and include numerous work examples that connect theory to practice. You’ll gain insights into cutting-edge quantum algorithms and their financial applications, positioning you well for research or industry roles at this emerging intersection.Why finance professionals should read this book:For quantitative finance professionals, this book offers a practical overview of how quantum computing might transform your industry. You’ll understand which financial problems are well-suited to quantum approaches and gain enough technical depth to have meaningful conversations with quantum specialists without needing to become a quantum physicist. The portfolio optimization and market generators sections are particularly relevant to everyday quant challenges.Why computer scientists and ML/AI experts should read this book:If you work in ML/AI, this book provides an excellent bridge to quantum machine learning. You’ll see how classical concepts like neural networks and generative models extend to the quantum realm, with clear explanations of the potential advantages in expressivity and training efficiency. The authors’ focus on hardware-efficient implementations ensures that the content remains relevant to practical computing constraints.While the book doesn’t shy away from mathematical formalism, the authors balance rigor and intuition. The prose is clear, the examples illuminate, and the historical context provides depth to the material.This book is an invaluable resource and a worthy addition to your professional library for anyone looking to understand how quantum computing might reshape quantitative finance in the coming years.

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2. Masterclass in Quantum Engineering
   Very rarely do I read something and think I am way over my head, but this was one of those. That shouldn’t deter you from reading “Quantum Machine Learning and Optimisation in Finance’ ( Packt , 2025) by Antoine Jacquier and Oleksiy Kondratyev but you should be prepared. The book is less finance concerns than a deep focus on quantum engineering with a division into two sections; basic quantum mechanics and gate model quantum computing. If you are looking for a deep dive into how quantum algorithms work, and setting up those circuits on hardware, this is the book for you.As an interesting note, the book’s first chapter stands alone and walks through some quantum reminders. This excellent starting point reminds of the notation, basic principles and some quantum mechanics. This chapter proves essential as the book rapidly moves into deeper waters.The first actual section includes four chapters; quantum annealing, quadratic unconstrained binary operations, boosting and boltzman machines. Each area shows the math, explains how the transformations work, shows the circuit building, and suggests some potential outcomes. These are roughly tied to financial outcomes with annealing as traveling salesman option and binary operations to select a number of assets from a larger set of financial possibilities. Quantum boosting can be used to accelerate loan applications with faster search results while Boltzman machines can train ML solutions.The next section then includes 10 chapters, each focused on a dedicated quantum solution. This ranged from an initial single qubit quantum circuit to multiple qubits and connected networks. The most directly connected to finance is the Quantum Circuit Born Machine as a market generator. This system used one and multi-qubit options to code markets. The explanation shows how a quantum neural network uses cost as a classification error while the QCBM defines cost as a distance between probability distribution. This allows using a genetic algorithm to effectively train the model, and distribute accurate results. Quantum boosting also plays a factor as the number of classification areas can be difficult in classical computing.I really enjoyed the book but one objection was the density of the academic material. This was truly a masterclass in building quantum circuits, establishing algorithms, and obtaining results. The academic focus meant the finance was frequently left behind. There were references suggesting different algorithms could be used for financial problems but the detailed case studies are missing.Overall, “Quantum Machine Learning and Optimisation in Finance’ (Packt, 2025) is a wonderful demonstration fo advanced concepts possible with quantum solutions. The missing parts were the links to finance, and the coding elements needed to virtually build those circuits. However, if you can make it through the book, it should be no problem converting those solutions into practice for financial practices. Integrating annealing, quantum machines, parameterized circuits, and quantum kernels are all options guaranteed to change the future. I recommend this book for serious practitioners in the quantum computing space, or those aspiring for a truly deep understanding.

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3. Pre-requisites required, intermediate reading book
   This is a groundbreaking book that offers an enthralling journey into the applications of Quantum Machine Learning (QML) in the finance sector. The authors have skillfully build the content from fundamental principles, postulates of quantum mechanics to advanced topics like quantum neural networks. While the book provides a comprehensive exploration, it is best suited for readers with some prior knowledge of quantum modeling and machine learning concepts. This is a intermediate-level book that serves as an excellent resource for those looking to deepen their understanding of QML in finance. Overall, it is highly recommended for finance professionals and data scientists looking to advance in this exciting field.

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