دانلود کتاب برنامه‌نویسی کوانتومی در عمق، حل مسائل با Q و Qiskit

I joined Microsoft Quantum and, by extension, the world of quantum computing in early 2017, just as the team started developing the quantum programming language that later became Q#. I spent a big part of the next eight years learning quantum computing myself and helping others do the same, both as part of my job at Microsoft and in the course I teach at Northeastern University. And, while doing this, I noticed several gaps in the way quantum computing was taught. First, a lot of material on quantum computing focused on its mathematical aspects only. I am an applied mathematician by training, so I’m comfortable with math. But I’m a software engineer by trade, and I’m a lot more comfortable with algorithms when I can implement them and experiment with running them! My first project in quantum computing education, the Quantum Katas, focused on introducing the basics—quantum states and gates, measurements, and simple algorithms—through a series of programming problems that the learner would solve to internalize the theory. The second gap shows up later in the learning journey. There are plenty of “quantum computing 101” resources that focus on introductory concepts. But, once you’ve mastered the basics, what’s next? What does “quantum computing 201” look like? If you’ve only ever run simple circuits, how do you start implementing real quantum algorithms? How do you test the quantum code you write? What do you need to know about quantum programming for future fault-tolerant quantum computers? And, ultimately, how do you figure out whether your quantum solution to the problem you’re looking at is going to do better than a classical algorithm? This book aims to answer these questions. The book offers you a selection of problems, from building quantum programming libraries to solving classical problems using quantum algorithms, and walks you through the solutions. As part of the discussion, I introduce the quantum software development workflow, from figuring out the algorithm suitable to solve the problem at hand to implementing the solution, testing it, and evaluating its performance. Each problem ends up being implemented as an end-to-end software project. Throughout the book I use Qiskit and Q#—two of the quantum programming languages used for quantum computing research today—to make sure the tools and methods I talk about are relevant to the current quantum programming landscape. There are, of course, plenty of topics I could not include in the book: domainspecific applications, the architecture of fault-tolerant quantum computers and the software stack we’re building for them, and debugging large-scale quantum algorithms, among others. I hope that this book will show you how to think about quantum computing as a software engineer and problem solver and thus equip you with the mindset you’ll need to continue your pursuit of knowledge!