Notice
As part of Phase 3 of our return to campus plan, most units will now run tutorials, seminars and other small group learning activities on campus for the second half-year, while keeping an online version available for those students unable to return or those who choose to continue their studies online.
To check the availability of face to face activities for your unit, please go to timetable viewer. To check detailed information on unit assessments visit your unit's iLearn space or consult your unit convenor.
Unit convenor and teaching staff |
Unit convenor and teaching staff
Thomas Volz
Daniel Burgarth
|
---|---|
Credit points |
Credit points
10
|
Prerequisites |
Prerequisites
Permission by special approval
|
Corequisites |
Corequisites
|
Co-badged status |
Co-badged status
PHYS7909
|
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 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: 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. |
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:
Individual oral examination via Zoom at the end of session. A demo oral exam will be provided on Zoom beforehand.
Computing lab held online (in the browser) via cocalc.com
Weekly to biweekly written assignments
Name | Weighting | Hurdle | Due |
---|---|---|---|
Oral examination | 40% | No | end of term |
Project report | 30% | No | weekly to biweekly |
Problem sets | 30% | No | second half of the session |
Assessment Type 1: Viva/oral examination
Indicative Time on Task 2: 20 hours
Due: end of term
Weighting: 40%
Oral examination in the Examination Period covering all course content
Assessment Type 1: Report
Indicative Time on Task 2: 32 hours
Due: weekly to biweekly
Weighting: 30%
Reports for numerical and computational projects
Assessment Type 1: Problem set
Indicative Time on Task 2: 30 hours
Due: second half of the session
Weighting: 30%
A sequence of problem sets throughout the session.
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
Delivery in 2020 will be managed via iLearn, with lectures delivered by both pre-recorded lectures on YouTube, live lectures on Zoom, and live seminars and discussions via Zoom.
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 NV spins, cavity QED, 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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Students seeking more policy resources can visit the Student Policy Gateway (https://students.mq.edu.au/support/study/student-policy-gateway). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
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