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

Getting started with quantum machine learning

In the previous section, you learned about the field of machine learning and the three most common algorithms used by machine learning researchers and by industries to accelerate growth. Every year, we are now witnessing a significant amount of growth of data worldwide, and the rate is going to increase even more in the near future. It has been estimated that about 60 zetabytes () has been accumulated by our planet!

Due to the growth of data and its associated features, the power of classical computers is becoming limited. We have powerful classical computers today that can deal with high-dimensional data, but a time is coming when classical computers won't be able to handle very high-dimensional data, and at that time quantum machine learning algorithms will have to step in.

The term quantum machine learning was coined by Lloyd, Mohseni, and Rebentrost in their paper Quantum algorithms for supervised and unsupervised machine...