Machine Learning with Quantum Computers
Machine learning is about making computers find structure and symmetries in data for solving problems instead of being explicitly programmed. Quantum computing is about using the laws of quantum mechanics to do computation. It is only natural to ask how they could be combined
Let's combine machine learning with quantum technology!
Quantum machine learning has the potential to be the next big thing in our society due to its parent disciplines. On the one hand there is a booming commercial interest in quantum technologies, which are at the critical point of becoming available for the implementation of quantum algorithms, and which have exceeded the realm of a purely academic interest. On the other hand, machine learning along with artificial intelligence is advertised as a central (if not the central) future technology into which companies are bound to invest to avoid being left out. Combining these two worlds invariably leads to an overwhelming interest in quantum machine learning.
If you have a background in either physics or computer science, then you are in Luck! Your sound understanding of linear algebra and computer algorithms allow you to be a part of this exciting new field!
This course is meant to be a guide to different ways in which we can do machine learning from a quantum computing perspective.
Planned course content
The course contains a series of hands-on lectures about current status in quantum machine learning. Please see https://github.com/osbama/Phys710 as an indicator of this years course content.
Syllabus
| Course Title | PHYS710 Machine Learning with Quantum Computers |
|---|---|
| Lecturers | Barış Malcıoğlu |
| Grading | Midterm %20, Term project %40, Hands-on sessions & homework %40 |
Hands-On sessions
- Attendance to all of the hands-on sessions, and submitting the assigned hands-on work is mandatory. Any missed hands-on session, or assigned hands-on work will result in N/A grade. Only officially documented cases (such as medical reports) will be considered for exemption.
Midterm Exam
- The midterm exam will involve a theory part and a programming part.
- The theory part should be answered using a Latex/Word processor, converted to pdf
- The programming part must be an ASCII text file containing python code (*.py).
- The files should be uploaded to supplied Turnitin interface. Any incompatible input will be disregarded.
Term projects
- The term project is the final exam.
- Participants are expected to present a project involving Quantum Computation, Quantum Communication, or Quantum hardware.
- The term project consists of these parts:
- A 1-page abstract describing the project
- Presentation (~20 minutes), Q&A session after the talk (~10 minutes)
- (Optional) A final report
- The presenter will be graded according to the scientific quality of the presentation
- The audience will be graded according to their participation in the Q&A session.
- The term projects will be presented in the last 3-4 weeks
- Attendance to the term project presentations is mandatory. The first missed week will result in a reduction of your final grade to %65. The second missed week will result in a reduction of your final grade to %35. If you miss three weeks, you will receive N/A grade.
- Only one missed week might be allowed with a valid official excuse.
Textbooks
Theory Content:
- "Machine Learning with Quantum Computers" Second Edition, Maria Schuld Francesco Petruccione https://doi.org/10.1007/978-3-030-83098-4
Lab Content:
More
Please consider opening an account in IBM Quantum Cloud, or Xanadu Cloud. You need to know python, have access to your own PC for homeworks, and at least libraries like pennylane or qiskit, also tensorflow and torch.