Quantum Computer Programming

Instructor

Fabio A. González
Programs: MSc & PhD en Ingeniería de Sistemas y Computación
Universidad Nacional de Colombia

Course goal

Quantum computing is a rapid developing field with a high potential to revolutionize the way we compute and how we use computers to solve problems. It exploits the particular nature of quantum systems to perform calculations that in some cases can exhibit exponential speed-ups with respect to classical computers. This course is an introduction to the mathematical tools necessary to describe quantum systems and the fundamental concepts of quantum circuits and algorithms. The course will provide tools that allow students to implement quantum circuits and algorithms using quantum computing libraries and to run them using quantum simulators as well as real hardware. The final part of the course will focus on the application of quantum computing to machine learning.

At the end of the course the student will be able:

• To describe the differences between quantum and classic computation.
• To understand the mathematical basis of quantum state representation and quantum operations.
• To build quantum circuits connecting different types of quantum gates.
• To implement quantum circuits and algorithms using quantum programming libraries.
• To run quantum circuits and algorithms using quantum simulators and real quantum computers.
• To understand how to design quantum algorithms to solve different problems.
• To understand how to design hybrid quantum algorithms to solve machine learning problems.

1 Quantum Computing Basis

1.1 Introduction to quantum computing

1.2 Linear algebra and Dirac notation

1.3 Qubits

1.4 Measurements

1.5 Composite systems

1.6 Pure, entangled and mixed states

1.7 Theory of computation and Computational Complexity

**1.8 Implementation of quantum computers **

2 Quantum circuits and algorithms

2.1 Quantum circuits and gates

2.2 Superdense coding and teleportation

2.3 Deutsch-Joza algorithm

2.4 Simon’s algorithm

2.5 Shor’s algorithm

2.6 Grover’s algorithm

2.7 Noisy intermediate scale quantum computers

2.8 Quantum algorithms for applications

3 Quantum machine learning

3.1 Machine learning review

3.2 Quantum measurement classification

3.3 Quantum machine learning algorithms

• Assignments 30%
• Presentation 20%
• Exam 30%
• Final project 20%

Grades

References

• [Nielsen10] Michael A. Nielsen & Isaac L. Chuang. Quantum Computation and Quantum Information. CAMBRIDGE UNIVERSITY PRESS. 2010
• [Asfaw19] Abraham Asfaw et al., Learn Quantum Computation using Qiskit. Qiskit Development Team. 2019
• [Kaye07] Phillip Kaye et al. An Introduction to Quantum Computing. Oxford University Press. 2007 9783319964232). Springer. 2018
• [Nannicini17] Nannicini, G. An introduction to quantum computing, without the physics. arXiv preprint arXiv:1708.03684. 2017
• [Rieffel11] Rieffel, E. G., & Polak, W. H. Quantum computing: A gentle introduction. MIT Press. 2011
• [Schuld18] Schuld, M., & Petruccione, F. Supervised Learning with Quantum Computers. Springer. 2018

Additional resources

• [IGFAE20] Andrés Gómez. Introduction to Quantum Computing. Short Course, Instituto Galego de Física de Altas Enerxías (IGFAE). 2020
• [CS191x] Umesh Vazirani. BerkeleyX CS191x: Quantum Mechanics and Quantum Computation. edX. 2013
• [CS269Q] Dan Boneh, Will Zeng, CS 269Q: Quantum Computer Programming, Stanford University, 2019
• [Qiskit] Qiskit: is an open source SDK for working with quantum computers at the level of pulses, circuits and algorithms. IBM.
• [IBMQE] IBM Quantum Experience: Learn, develop, and run quantum programs.