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.

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Table of Contents (19 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
1
Section 1: Essential Background and Introduction to Quantum Computing
Section 1: Essential Background and Introduction to Quantum Computing
Free Chapter
2
Chapter 1: Essential Mathematics and Algorithmic Thinking
Chapter 1: Essential Mathematics and Algorithmic Thinking
Introducing linear algebra for quantum computing
Coordinate systems, complex numbers, and probability
Defining computational thinking
Introducing computer algorithms
The calculation of the time and space complexity of algorithms
Summary
Further reading
3
Chapter 2: Quantum Bits, Quantum Measurements, and Quantum Logic Gates
Chapter 2: Quantum Bits, Quantum Measurements, and Quantum Logic Gates
Introducing single quantum bits – qubits and superposition of qubits
Illustrating qubits in different bases
Introducing quantum measurements
Quantum logic gates
Summary
Further reading
4
Chapter 3: Multiple Quantum Bits, Entanglement, and Quantum Circuits
Chapter 3: Multiple Quantum Bits, Entanglement, and Quantum Circuits
Introducing multiple quantum bits
The wonder of quantum entanglement
Exploring multi-qubit quantum logic gates
Quantum teleportation
Quantum superdense coding
Summary
5
Chapter 4: Physical Realization of a Quantum Computer
Chapter 4: Physical Realization of a Quantum Computer
Criteria for quantum computation existence
Superconducting qubit-based quantum computers
Quantum annealing-based quantum computers
Ion-trap quantum computers
Nuclear magnetic resonance
Optical photonics-based quantum computers
Summary
Further reading
6
Section 2: Challenges in Quantum Programming and Silq Programming
Section 2: Challenges in Quantum Programming and Silq Programming
7
Chapter 5: Challenges in Quantum Computer Programming
Chapter 5: Challenges in Quantum Computer Programming
A brief history of classical computers
The challenges of today's classical computers
Understanding the assembly language
HLLs for classical computers
Low-level circuit programming of quantum computers
Introducing quantum programming languages
The challenges of quantum programming
Summary
Further reading
8
Chapter 6: Silq Programming Basics and Features
Chapter 6: Silq Programming Basics and Features
Technical requirements
Introducing Silq and its special features
Installing Silq
Introducing Silq data types
Defining variables in Silq
Control flow in Silq
Functions and iterations in Silq
Introducing Silq annotations
Simple example programs using Silq
Summary
Further reading
9
Chapter 7: Programming Multiple-Qubit Quantum Circuits with Silq
Chapter 7: Programming Multiple-Qubit Quantum Circuits with Silq
Technical requirements
Exploring multi-qubit quantum logic gates in Silq
Constructing quantum circuits with quantum logic gates using Silq
Quantum teleportation
Quantum superdense coding
Summary
10
Section 3: Quantum Algorithms Using Silq Programming
Section 3: Quantum Algorithms Using Silq Programming
11
Chapter 8: Quantum Algorithms I – Deutsch-Jozsa and Bernstein-Vazirani
Chapter 8: Quantum Algorithms I – Deutsch-Jozsa and Bernstein-Vazirani
Technical requirements
Introducing quantum parallelism and interference
Implementing the Deutsch-Jozsa algorithm
Implementing the Bernstein-Vazirani algorithm
Summary
12
Chapter 9: Quantum Algorithms II – Grover's Search Algorithm and Simon's Algorithm
Chapter 9: Quantum Algorithms II – Grover's Search Algorithm and Simon's Algorithm
Technical requirements
Introducing search algorithms
Getting started with Grover's search algorithm
Grover's search for one solution using Silq programming
Grover's search for multiple solutions using Silq programming
Grover's search for an unknown number of solutions using Silq programming
Introducing Simon's algorithm
Summary
Further reading
13
Chapter 10: Quantum Algorithms III – Quantum Fourier Transform and Phase Estimation
Chapter 10: Quantum Algorithms III – Quantum Fourier Transform and Phase Estimation
Technical requirements
Introducing the classical Discrete Fourier Transform (DFT)
Exploring the QFT
Implementing the QFT using Silq
Getting started with the phase estimation algorithm
Implementing phase estimation using Silq
Summary
Further reading
14
Section 4: Applications of Quantum Computing
Section 4: Applications of Quantum Computing
15
Chapter 11: Quantum Error Correction
Chapter 11: Quantum Error Correction
Technical requirements
Introducing classical error correction technique
Understanding quantum error correction
Summary
16
Chapter 12: Quantum Cryptography – Quantum Key Distribution
Chapter 12: Quantum Cryptography – Quantum Key Distribution
Technical requirements
Introducing classical cryptography techniques
Understanding quantum key distribution
Summary
17
Chapter 13: Quantum Machine Learning
Chapter 13: Quantum Machine Learning
Introducing classical machine learning
Getting started with quantum machine learning
Learning about the quantum K-means algorithm
Exploring variational circuits
The latest developments in the field of quantum machine learning and quantum computing
Summary
Further reading
Why subscribe?
18
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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...

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