QA-201 · Applied course

Quantum Optimisation and Hybrid Algorithms

Formulate a small optimisation problem and critically evaluate a variational quantum approach against classical baselines.

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Preparation and learning goals

Entry requirements

Circuit programming, probability and classical optimisation fundamentals.

What you will work towards

Formulate a small optimisation problem and critically evaluate a variational quantum approach against classical baselines.

The practical task asks you to apply the concepts, justify your choices and communicate the assumptions and limits of your result.

Course outline

MODULE 01

Problem encoding and cost functions

MODULE 02

variational circuits and optimisers

MODULE 03

noise, sampling and baseline selection

The outline defines this course’s intended scope. The detailed teaching sequence and preparation materials will be confirmed for the agreed delivery.

Practical work

A small optimisation study that reports objective values, computational effort and sensitivity to experimental choices.

Technical work is based on simulation, calculation or protocol analysis. Hardware access and laboratory sessions require a separately confirmed arrangement.

Assessment and feedback

The proposed review criteria are conceptual accuracy, a defensible method, clear evidence and appropriate treatment of assumptions and uncertainty.

Assessment format and feedback arrangements will be agreed before booking. No accreditation or external certification is claimed.

Discuss delivery and preparation

This is a course outline available for enquiry. Instructor, dates, duration, delivery format, fees and any hardware requirements will be confirmed in a written proposal before a booking is made.

Tell us whether you are learning independently, developing a team or planning research training. Submitting this form does not create a booking or payment obligation.

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Quantum Optimisation and Hybrid Algorithms · Applied

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