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 the classical Discrete Fourier Transform (DFT)

Before we dive into the QFT, it is important to know about the DFT because the QFT is derived from the DFT. The DFT is known as the frequency domain representation of an input sequence. It is used in the spectral analysis of various signals, such as reducing the variance of a spectrum. It is also utilized in the lossy compression of image and sound data. Apart from the previous applications mentioned, the DFT is also used in mathematics, such as for solving partial differential equations and in polynomial multiplication as well. Since the Discrete-Time Fourier Transform (DTFT) of a sequence represents a continuous and periodic transformation of the input sequence, if we sample the DTFT at periodic intervals, we get the DFT of the input sequence.

Mathematically, the DFT is defined by the following equation:

From the mathematical equation of the DFT, it is clear that there is a mapping from the input...