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

Learning about the quantum K-means algorithm

We have already discussed the classical K-means clustering algorithm, and that will now help you to understand the quantum version of the K-means algorithm. As you already know, with the increase in the dimensions of big data recently, classical computers are becoming slower at processing data, and the same applies to the K-means algorithm as well. It has been found that the classical version of K-means has a time complexity of , where N is the number of features of the data points, M is the total number of input data points, and K is the number of clusters. However, with the quantum K-means algorithm, we get a time complexity of because only qubits are required to load the N-dimensional input data points using the amplitude encoding technique.

For the implementation of the quantum K-means algorithm, three main components are utilized – the swap test circuit, the distance calculation circuit, and Grover's optimization circuit...