# **PHYS 710**: Quantum Machine Learning for Sciences



**Target Audience:** M.S. and Ph.D. students in Engineering and Natural Sciences.


This course is designed for graduate students who wish to explore the exciting and rapidly developing field of Quantum 
Machine Learning (QML). As we are in the midst of a second quantum revolution, the synergy between quantum computing and
machine learning is paving the way for groundbreaking advancements in various fields, from drug discovery and materials 
science to various optimization problems.

For **physicists and natural science students**, this course will provide a solid foundation in machine learning 
techniques and demonstrate how they can be applied to solve complex quantum mechanical problems. You will learn how to 
leverage the power of quantum computers to analyze quantum systems and to design new quantum algorithms.

For **engineering students**, this course will offer a unique opportunity to understand the principles of quantum 
mechanics and to apply them to machine learning. You will gain a competitive edge by learning how to design and 
implement QML algorithms that can potentially outperform classical machine learning models on certain tasks.

This is a hands-on course where you will learn by doing. Through a series of Jupyter notebooks and practical 
exercises using the Qiskit framework, you will gain practical skills that are highly sought after in both academia and 
industry. By the end of this course, you will be well-equipped to contribute to this cutting-edge field and to apply QML techniques to your own research.

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**Course Code:** 2300710 

**Course Title:** Quantum Machine Learning for Sciences

**Course Type:** Elective

**Instructional Methods:**
* Interactive lectures with live coding demonstrations.
* Hands-on sessions with Jupyter notebooks.
* Problem-solving sessions.

**Assessment Methods:**
* **Assignments/Midterm (35%):** Regular programming assignments to reinforce concepts and build Qiskit proficiency.
* **Final Project (65%):** A small project involving the implementation and analysis of a quantum algorithm or protocol of the student's choice.

**Prerequisites:**
A basic familiarity with Python programming, machine learning principles, quantum physics, and quantum information theory.  


**Contents:** https://github.com/osbama/Phys710