دانلود کتاب راهنمای عملی محاسبات کوانتومی – رویکرد عملی به محاسبات کوانتومی با Qiskit

Almost three years ago, we sat down to write a little book (well, at that time, we thought it would be little) titled A Practical Guide to Quantum Machine Learning and Quantum Optimization. It was a new, exciting and unforgettable adventure, and we had tons and tons of fun because we made it. . . for us. It is a cliché to say that you should write the book that you would like to read, but, what can we say? We are all for clichés and that’s what we did. We wrote the book that we would’ve liked to read when we started studying quantum machine learning and quantum optimization. A book with mathematical rigor, because you cannot understand quantum computing without all the formulas, but also with code that you can run to obtain results, because quantum computing should be, first and foremost, about computing. So we wrote the book mainly for us. . . and we still can’t believe how many people found our way of explaining quantum computing useful and even entertaining. Since the book was published, we’ve received feedback from many readers that liked the book and, to our surprise, wanted more. At the same time, quantum computing has surged in popularity. In the last few years, more and more people have became interested in learning how to program quantum computers and the number of courses on the topic offered by universities all over the world has grown exponentially. Moreover, with 2025 proclaimed by the United Nations as the International Year of Quantum Science and Technology, this trend will very likely continue and multiply. For all these reasons, even if we believed that writing A Practical Guide to Quantum Machine Learning and Quantum Optimization had been a once-in-a-lifetime adventure, when the wonderful people at Packt reached to us again wondering whether we’d be interested in writing a new book, we thought that it was a brilliant idea. In fact, we still had a thing or two to write about quantum computing, so why not? In our previous book, we decided to focus on what we call “modern” quantum algorithms: variational algorithms, quantum annealing, and other techniques that try to put the kind of quantum computers that are available today to good use in tasks such as data classification and combinatorial optimization. But, in fact, the development of quantum computing as a field did not start with these kinds of applications in mind. Algorithms such as the ones proposed by Grover, Shor, and other visionaries, are quite different from the ones that we presented in our previous book. . . but equally important and fascinating. In A Practical Guide to Quantum Machine Learning and Quantum Optimization we decided to skip many wonderful applications of quantum computing to focus on these modern quantum algorithms. And, though we of course love all the new developments that the quantum computing community has come up with in recent years, we’ve always had a bit of a regret for not having been able to include other, more foundational algorithms and protocols. Understanding how the quantum Fourier transform works or how quantum teleportation is achieved is not only fascinating in itself, but crucial to comprehend what quantum computers can do and how they do it. It is obviously great to be able to apply variational circuits to learn patters in datasets or to find ground states of Hamiltonians, but if you’ve never heard of Deutsch’s algorithm or you do not know how to implement Grover’s search, your grasp of quantum computing will be inevitably incomplete. With the book that you now hold in your hands, we try to put a remedy to that. Our focus is, now, on the foundations of quantum computing. In some sense, you could think of this new book as a prequel to our previous one. If you’ve already read A Practical Guide to Quantum Machine Learning and Quantum Optimization, you will find here some concepts that you already know about. And you will learn much more about them, in the same way that you learned much more about Obi-Wan Kenobi and Anakin Skywalker when you watched The Phantom Menace after being acquainted with them from the original Star Wars trilogy1. And the great thing is that, if you’ve never read our previous book, you will not miss anything! Our approach to explaining the topics covered in this book is, essentially, the same that we followed in A Practical Guide to Quantum Machine Learning and Quantum Optimization. We start completely from scratch and we do not assume any knowledge of quantum physics. In fact, we can assure you that quantum physics is even less necessary to understand this book than it was for our previous one. And you don’t need to know a lot of math either—just some basic algebraic concepts, such as what a matrix and a vector are, and how to multiply them. By the way, if you need a refresher on these or other mathematical concepts, you can always refer to the Appendices at the end of the book. Another key characteristic of our approach to introducing concepts is that we firmly believe in the importance of a gradual progression—from simple ideas to more complex ones—allowing you to build confidence and understanding step by step. We start very humbly, focusing on a simple qubit. And we spend a lot of time studying one-qubit systems before moving further. But we think you’ll find this approach helpful in developing a deep understanding that serves as a solid foundation for more complex algorithms and techniques.