Book Image

Quantum Computing with Silq Programming

By : Srinjoy Ganguly, Thomas Cambier
Book Image

Quantum Computing with Silq Programming

By: Srinjoy Ganguly, Thomas Cambier

Overview of this book

Quantum computing is a growing field, with many research projects focusing on programming quantum computers in the most efficient way possible. One of the biggest challenges faced with existing languages is that they work on low-level circuit model details and are not able to represent quantum programs accurately. Developed by researchers at ETH Zurich after analyzing languages including Q# and Qiskit, Silq is a high-level programming language that can be viewed as the C++ of quantum computers! Quantum Computing with Silq Programming helps you explore Silq and its intuitive and simple syntax to enable you to describe complex tasks with less code. This book will help you get to grips with the constructs of the Silq and show you how to write quantum programs with it. You’ll learn how to use Silq to program quantum algorithms to solve existing and complex tasks. Using quantum algorithms, you’ll also gain practical experience in useful applications such as quantum error correction, cryptography, and quantum machine learning. Finally, you’ll discover how to optimize the programming of quantum computers with the simple Silq. By the end of this Silq book, you’ll have mastered the features of Silq and be able to build efficient quantum applications independently.
Table of Contents (19 chapters)
Section 1: Essential Background and Introduction to Quantum Computing
Section 2: Challenges in Quantum Programming and Silq Programming
Section 3: Quantum Algorithms Using Silq Programming
Section 4: Applications of Quantum Computing

Introducing classical machine learning

Machine learning refers to the science and engineering of building intelligent machines that can perform different kinds of tasks without being explicitly programmed to do so. In other words, we can define machine learning as the study of algorithms and statistical models that are used to solve a particular problem without being programmed and that only rely on patterns and inferences from data. Since classical computers (operating on classical bits) are used to do machine learning, it is called classical machine learning.

With that definition of machine learning, you can see that the machine is not actually learning; rather, it searches for a mathematical relation in some input data that will result in an output called a prediction. Machine learning is a marketing term coined by Arthur Samuel from IBM in 1959 to attract potential industries and customers to use this technology. In essence, machine learning is nothing but a composition of mathematics...