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

Exploring variational circuits

In this section, we will be diving into the concept of variational circuits, which form an integral part of today's quantum machine learning algorithms and research. We will be briefly looking into the three most common variational methods – variational quantum classifier (VQC), variational quantum eigensolver (VQE), and quantum approximation optimization algorithm (QAOA). Let's start with VQC in the next section.

Variational quantum classifier (VQC)

VQC, as the name suggests, is a classifier that is composed of quantum circuits with variational parameters or trainable parameters. This is the basis for quantum neural networks. Since you are already familiar with the concepts of artificial neural networks, it will be easy for you to appreciate the nature of the variational circuit used for classifiers.

There are many different types of variational circuits available that can be used for quantum classifiers, and many of the circuit...