Description of Course

Quantum computing is an exciting emerging field. Its computational paradigm relies on principles of quantum mechanics - one of the most counter-intuitive ways to describe our world. As a relatively new scientific area, its clear interpretations are not yet widely spread. Many terms, such as superposition and entanglement, are widely discussed but often poorly understood.

Quantum computing is an interdisciplinary field that lies at the intersection of quantum physics, computer science, and mathematics. The principle of quantum computation, or quantum information it may be hard to grasp what this means without too much theoretical background. But, understanding the quantum nature of information processing offers a wonderful opportunity for life scientists to collaborate with physicists, quantum computing programmers to solve problems of biology.

The course material is designed for those life scientists who are interested in this subject at an introductory level but believe that there's something else than just classical machine learning and programming, that will move our computing power further into infinity. For those who are interested in quantum computing but who are not necessarily comfortable reading expositions of the theoretical aspects of quantum information and computation at the graduate level, this course is an excellent choice. This course aims to give the student an introduction to this unusual new field.

Topics

1. Quantum Computation: History & Overview (7/3 Thr) - motivation, foundations, and prominent applications 

[Link1]

2. Essential math, linear algebra, and basic Python computing language (7/8 Tue)

3. Quit, Bloch sphere, complex number, and Euler's equation (7/10 Thr) 

4. Quantum gates, concepts of superposition and entanglement (7/15 Tue)

5. IBM Qiskit system, H gates, and quantum random number generator (7/17 Thr)

6. Prime factorization via quantum computing, quantum computer as an adding machine (7/22 Tue)

7. (7/24 Thr)

8. Quantum vs. classical machine learning, quantum neural network (7/29 Tue)

9. Applications of quantum computing in cryptography (Shor's algorithm) and chemistry (7/31 Thr)

10. Applications of quantum computing to biochemical systems, single-cell gene regulatory networks (8/5 Tue)

11. Final discussion (8/7 Thr)

Class Notes and Resource Materials

Class notes are distributed in class. There is no required textbook.

Additional readings: you are responsible for additional readings that will be announced in class.