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

Chapter 13: Quantum Machine Learning

With the growth of classical computers in every industry and various applications, a lot of data has been generated over the past few years. The advent of powerful classical computing, through things such as Graphics Processing Units (GPUs), has enabled many industries to work with large amounts of data efficiently in a small amount of time. But we have started to observe that the amount of data is growing rapidly, and it is going to increase further in the coming years, which will mean that classical computing methods will take longer to process and extract useful information from data. Quantum machine learning holds promise in this field, in that it can bring the power of quantum computing to the classical machine learning techniques used today due to the incredibly speedy parallel nature of the computation of quantum computers.

In this chapter, you will explore the concepts of classical machine learning methods and develop practical skills...