Unit convenor and teaching staff |
Unit convenor and teaching staff
Thomas Volz
Daniel Burgarth
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Credit points |
Credit points
10
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Prerequisites |
Prerequisites
PHYS714 or PHYS7905
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Corequisites |
Corequisites
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Co-badged status |
Co-badged status
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Unit description |
Unit description
The aim of quantum control is to drive a quantum system to a desired state or more generally evolution through pulse-shaping. The unit begins with introducing control theory as a subject from engineering and a tool for solving inverse problems. We will discuss linear control and bilinear control, both are important in the quantum case. We then look at the Schrodinger equation as a bilinear control problem and aim to characterise what kind of states and operations can be reached in a given system. This leads us to an algebraic description of control, provided in the framework of Lie algebras. We will look at examples of how this works in practice in quantum computing. In such examples, one often encounters noise, and we will see how quantum control can help lowering noise, which leads us the control of open systems. A particular case of open system control is important in continuous variable quantum optics and known as the input-output formalism, which will bring us back to linear control. In the final part we introduce optimal control. The task here is to find the best way of controlling quantum system - shortest time, lowest energy, lowest noise. We look at examples from Nuclear Magnetic Resonance, from Ultrafast Laser Control, and from Quantum Computing. You will use the python library "QuTiP" to get experience with the beauty and the challenges of optimal control.
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Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
Late Assessment Submission Penalty: From 1 July 2022, Students enrolled in Session based units with written assessments will have the following university standard late penalty applied. Please see https://students.mq.edu.au/study/assessment-exams/assessments for more information. Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark) will be applied each day a written assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of '0' will be awarded even if the assessment is submitted. Submission time for all written assessments is set at 11:55 pm. A 1-hour grace period is provided to students who experience a technical concern. For any late submission of time-sensitive tasks, such as scheduled tests/exams, performance assessments/presentations, and/or scheduled practical assessments/labs, students need to submit an application for Special Consideration.
• Type of assessments in this unit and specific late policy
Problem sets – YES, Standard Late Penalty applies
Project reports - YES, Standard Late Penalty applies
Oral exam - not applicable
Name | Weighting | Hurdle | Due |
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Problem sets | 30% | No | to be determined |
Project reports | 30% | No | to be determined |
Oral Final Exam | 40% | No | to be determined |
Assessment Type 1: Problem set
Indicative Time on Task 2: 30 hours
Due: to be determined
Weighting: 30%
A sequence of problem sets throughout the session.
Assessment Type 1: Report
Indicative Time on Task 2: 32 hours
Due: to be determined
Weighting: 30%
Reports for numerical and computational projects
Assessment Type 1: Viva/oral examination
Indicative Time on Task 2: 20 hours
Due: to be determined
Weighting: 40%
Oral examination in the University Examination period.
1 If you need help with your assignment, please contact:
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation
The unit will be delivered exclusively online (via Zoom).
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Unit information based on version 2022.02 of the Handbook