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

Quantum annealing-based quantum computers

Quantum annealing is a technique used to find the global minimum or optimal solution to a problem that can have a large number of solutions. Essentially it is used for optimization problems such as the Traveling Salesman Problem (TSP), which seeks to find the shortest path for a traveling salesman to visit every city on the route only once and then return back to their starting city. The TSP sounds very simple in description but is a very hard problem to solve because as the number of cities increase, thousands, millions, or even billions of paths emerge as well, which makes this a combinatorial optimization problem. For this kind of problem, quantum computers are the best because the TSP cannot be solved even by today's fastest computers.

D-Wave, a Canadian company, uses the quantum annealing technology to solve combinatorial optimization problems and their computers have been purchased by Google, Microsoft, and NASA for research...