Learn Quantum Computing with Python and IBM Quantum: Write your own practical quantum programs with Python

Very user friendly book that introduces people to Python and quantum. Using this book to prepare for the qiskit certification.The book is very detailed yet is not daunting. Lots of picture examples and step by step guide on how to use the IBM's IQP.

I've been interested to know more about quantum computing. This book was a great resource to learn more about what it can do. I feel there's so much info about AI, but not so much about quantum computing, so it's good to see there's more up to date books about quantum computing, especially with examples in Python.

As the 'singular Post AI-Quantum Pioneer', as recognized by xAI’s Grok AI, and inventor of Quantum-Augmented Self-Adaptive Networks (QASANs), I am thrilled to offer my resounding endorsement of Robert Loredo’s recently released second edition of "Learn Quantum Computing with Python and IBM Quantum". This is not merely a book; it is a meticulously crafted gateway into the fascinating and transformative realm of quantum computing, expertly guided by the IBM Quantum Ambassador worldwide lead himself, a true visionary and Qiskit advocate.Having personally engaged with IBM's Quantum Information Science Kit (Qiskit) and the IBM Quantum platforms, including exploring the potential of quantum algorithms for high-stakes applications in finance such as by institutions like JP Morgan having advanced beyond the learning curriculum, I can attest to the critical importance of resources that bridge the gap between theoretical concepts and practical implementation. Loredo’s work achieves this with exceptional clarity and precision. The book’s focus on practical quantum programming with Python, leveraging the power of IBM Quantum’s tools and Qiskit, is precisely what the industry needs to accelerate the journey towards quantum advantage and, crucially, quantum utility.What truly distinguishes this second edition is its comprehensive yet accessible approach. It doesn't shy away from fundamental principles, such as superposition, entanglement, and interference, providing clear explanations and practical examples that resonate with both newcomers and seasoned professionals seeking to deepen their understanding. The book’s structure, starting with an exploration of the IBM Quantum tools and progressing through quantum circuit creation with the IBM Quantum Composer to the intricacies of Qiskit programming, ensures a logical and engaging learning curve.The author’s deep expertise, evidenced by his role as a Master Inventor with over 250 patents, shines through in the insightful explanations and the practical guidance offered throughout the book. The inclusion of technical requirements at the beginning of each chapter, the provision of source code on GitHub, and the well-structured questions at the end of each chapter underscore the book’s commitment to fostering a hands-on learning experience.The exploration of key concepts like qubits, quantum logic gates, and the intricacies of programming with Qiskit are handled with remarkable pedagogical skill. The book delves into crucial aspects such as customizing and optimizing quantum circuits, understanding job components, and even generating pulse schedules on hardware, providing a holistic view of the quantum computing landscape as it exists today on the IBM Quantum platform.Furthermore, the book’s treatment of advanced topics such as simulating quantum systems and noise models, and the critical area of suppressing and mitigating quantum noise using the Qiskit Runtime service, is particularly timely and relevant in the current era of quantum utility. The discussion of foundational quantum algorithms like Deutsch-Jozsa algorithm and Bernstein-Vazirani algorithm, as well as more advanced algorithms like Quantum Fourier Transform and Grover’s search algorithms, provides a solid foundation for understanding the power and potential of quantum computation.The introduction of Qiskit Patterns in the final chapter is a forward-thinking addition, aligning perfectly with the need for higher-level abstractions that enable computational scientists to integrate quantum routines into their existing applications without getting bogged down in hardware-level details. This top-down approach is crucial for accelerating the adoption and application of quantum computing across various industries.The inclusion of appendices with valuable resources, including links to the IBM Quantum Learning Platform, Qiskit documentation, GitHub repository, Slack community, and the Quantum Algorithm Zoo, further enhances the book’s value as a comprehensive guide for continuous learning and engagement with the quantum ecosystem.In conclusion, Robert Loredo has delivered an exceptional resource that empowers practitioners, researchers, scientists, faculty, and students alike to navigate the exciting world of quantum computing with confidence and competence. This second edition of "Learn Quantum Computing with Python and IBM Quantum" is an indispensable guide for anyone serious about harnessing the transformative power of quantum computation.Therefore, with immense enthusiasm, I wholeheartedly recommend this book.Finally, I extend my enthusiastic encouragement to the dedicated Post AI-Quantum Computing teams at world-leading AI-Quantum Technology and Banking & Finance firms such as our clients over last three decades including Amazon, Google, IBM, Microsoft, and JP Morgan among others. To further advance your groundbreaking R&D practices, I urge you to explore the adoption and adaptation of our “three decades of cohesive innovation” on “self-adaptive quantum augmented systems” [as concluded by xAI's Grok AI] as detailed in our forthcoming full research paper on Quantum Augmented Self Adaptive Networks, QASANs accepted for presentation at the 20th Annual Symposium on Information Assurance (ASIA '25) conference co-sponsored by the New York State and State University of New York (SUNY) Albany at the New York State Capitol, accessible from my personal home page, my online full-text downloadable publications and presentations lists on my personal Web domain and my author pages such as on arxiv, SSRN, and ORCID as well as my LinkedIn feeds. The principles of Quantum-Augmented Self-Adaptive Networks (QASANs), born from a 30-year arc of pioneering post AI-quantum R&D, offer a powerful paradigm for building resilient and adaptable post AI-quantum systems that can effectively manage quantum uncertainty and time-space complexity – truly building human-centered meaning-aware ‘quantum minds’ for managing ‘quantum uncertainty’ in a post AI-quantum future. The insights gained from this book, coupled with the innovative framework of QASANs, can undoubtedly pave the way for unprecedented advancements in the Post AI-Quantum Era.- Dr.-Eng.-Prof. Yogesh Malhotra ‘Yogi’AWS Partner, Global Risk Management Network, LLCQuantum-Augmented Self-Adaptive Networks (QASANs) Inventor'Singular Post AI-Quantum Pioneer'- xAI’s Grok AI:‘30-Years of cohesive Post AI-Quantum R&D innovation’MIT-Princeton AI-Quantum Finance Faculty-SMER&D Impact among AI & Quant Finance Nobel Laureates

Quantum is fascinating and I enjoy every opportunity to learn about the process. “ Quantum Computing with Python and IBM Quantum, 2nd Ed” (Packt, 2025) by Robert Loredo revises the earlier book and makes excellent use of the IBM Quantum platform. The book is chock-full of examples direct from the website making it easy to understand and follow the samples on a separate screen as one reads. Each chapter includes an assessment, and the journey begins with quantum basics, expands into some Qiskit fundamentals, and finishes with detailed discussions on reducing noise and implementing algorithms.Normally, early chapters just revisit basic skills. In this case, the first two chapters are a deep dive into the IBM Quantum platform. I really enjoyed using the platform, following along, and the ease of taking my coded samples onto an actual quantum computer. One warning: playing with samples and repetitions can easily lead to charges, and although .001 cents per execution may seem low when the algorithms run upwards of 10,000 executions on average, prices can stack up quickly. Just make sure to keep yourself in the free mode or charge to the corporate learning budget.Once installing and configuring Qiskit is covered, the subjects rapidly turn to understanding and building various quantum circuits with the tools. Qiskit is an open-source software development kit (SDK) to create, manipulate and run quantum programs on IBM simulators. I’ve also used Q#, QCL, and Silq but Qiskit is specifically designed for the IBM suite, making it extremely useful. The Qiskit tool splits coding into three areas: developer, algorithm, and model. Each has different advantages in places based on what one intends the code to accomplish.One of my favorite blocks was the section on optimization and noise mitigation. Normally, when running code, noise and decoherence effects are ignored as one concentrates solely on the coded expression. When running on a quantum simulator, these effects are negligible. However, since the IBM kit allows running on hardware, understanding noise effects can be essential. One of the keys to a cost-effective quantum program is to run as many simulations on classical versions as possible to minimize the need for error correction on the more expensive machines. These chapters emphasize how the Qiskit coding model for mapping, optimizing, executing and post-process excels at identifying sources of noise before reaching the actual machine.An area I liked and disliked about the book was the large number of graphics. These are an essential part of learning, but at times, they also felt overwhelming. On average, graphics are about 40% of each chapter, with code as another 20-30%. Depending on your learning style, this approach can be positive or negative. The graphics tie tightly to the IBM platform but there are some spots where a little more explanation of what was happening may have been beneficial.Overall, Quantum Computing with Python and IBM Quantum, 2nd Ed” (Packt, 2025) by Robert Loredo is an excellent improvement to the first edition, which was published in 2020. It revisits all the needed essentials, shows how tools have improved, and highlights changes. Chapter 10, on suppressing and mitigating quantum noise, was completely new as the technology moved from theoretical applications to actually working with Qiskit to resolve issues. Overall, if you are doing any type of quantum programming, or just interested in getting started, this makes an excellent reference for your shelf.

This is a standout book for anyone interested in quantum computing. It expertly simplifies complex concepts like superposition, interference, and entanglement. The book truly shines in its hands-on approach, guiding readers to create and visualize quantum gates and circuits via the IBM Quantum Composer, and even run experiments on real quantum computers.The author also clarifies quantum algorithms and error mitigation techniques, vital components in this field. The progressive learning path makes it accessible, despite requiring some background in computer science.This book is a must-read for Python developers and those interested on quantum computing.