Book Image

Dancing with Python

By : Robert S. Sutor
Book Image

Dancing with Python

By: Robert S. Sutor

Overview of this book

Dancing with Python helps you learn Python and quantum computing in a practical way. It will help you explore how to work with numbers, strings, collections, iterators, and files. The book goes beyond functions and classes and teaches you to use Python and Qiskit to create gates and circuits for classical and quantum computing. Learn how quantum extends traditional techniques using the Grover Search Algorithm and the code that implements it. Dive into some advanced and widely used applications of Python and revisit strings with more sophisticated tools, such as regular expressions and basic natural language processing (NLP). The final chapters introduce you to data analysis, visualizations, and supervised and unsupervised machine learning. By the end of the book, you will be proficient in programming the latest and most powerful quantum computers, the Pythonic way.
Table of Contents (29 chapters)
Part I: Getting to Know Python
PART II: Algorithms and Circuits
PART III: Advanced Features and Libraries
Other Books You May Enjoy
Appendix C: The Complete UniPoly Class
Appendix D: The Complete Guitar Class Hierarchy
Appendix F: Production Notes

3.7 Nested comprehensions

You can nest comprehensions to create lists within lists. Our task in this section is to explore several ways of making this list of four lists, each of which has four numbers:

Creating an identity matrix

On the left is the list of lists, and on the right is a matrix. In particular, it is a 4 by 4 identity matrix, as you see in linear algebra. The matrix is diagonal: the only non-zero entries are on the main diagonal. Coders frequently use lists of lists to implement matrices in Python.

Linear algebra is at the heart of quantum computing and many other disciplines. For now, we look at how to build the list.

In this case, it is not onerous to type in the list, but we use matrices of size 2n by 2n in quantum computing. When n is 15, we get a 32768 by 32768 matrix, for example.

Let’s begin by creating a 4 by 4 list of lists filled with zeros.

m = [[0 for i in range(4)]...