Though quantum computing has had some limited uptake, it is not yet in the mainstream. In time, it  will move business, science, and government forward in unprecedented ways by solving problems that are too complex for today’s computational systems.

Imagine  building a house, with a full list of requirements for that house, but budget constraints prevent purchasing them.  What is necessary is  to work out the optimum combination of items which gives  the best value for money.

This is an example of an optimisation problem in which it is necessary to discover a best combination of things given some constraints. Typically, this sort of problem is very difficult to solve  because of the huge number of possible combinations.

Otimisation problems exist in many different domains: systems design, mission planning, airline scheduling, financial analysis, web search, cancer radiotherapy.  The list is endless adn encompasses some of the most complex problems in the world. The potential benefits to businesses, people and science will be enormous if optimal solutions can be readily computed.

When a human looks at a  photograph, he or she can efortlessly isolate the different objects in the image: trees, mountains, people, etc. While a simple process for people, this is in fact a hugely difficult task for computers . This is because programmers do not know how to define the essence of a, for example, a ‘tree’ in computer code.

Machine learning is the most successful approach to solving this problem, in which programmers write algorithms that automatically learn to recognize the ‘essences’ of objects by detecting recurring patterns in huge amounts of data. Because of the amount of data involved in this process, and the immense number of potential combinations of data elements, this is a very computationally expensive optimisation problem.

Many things in the world are uncertain, and governed by the rules of probability. We have, in our heads, a model of how things will turn out in the future, and the better our model is, the better we are at predicting it. We can also build computer models to try and capture the statistics of reality. These tend to be very complicated, involving a huge number of variables.

In order to check to see if a computer’s statistical model represents reality, we need to be able to draw samples from it, and check that the statistics of our model match the statistics of real world data. The Monte Carlo simulation, which relies on repeated random sampling to approximate the probability of certain outcomes, is an approach used in many industries such as finance, energy, manufacturing, engineering, oil & gas production and the environment. For a complex model, with many different variables, this is a difficult task to do quickly.

The major assessment task associated with this course involves a thorough and complete analysis of ONE major application of quantum computing. The assessment task is made up of two components.

1.     Australian Curriculum Assessment and Reporting Authority, 2012, The Australian Science Curriculum Years 7-10 Version 4.0, Sydney, Australia.

2.     Board of Studies NSW, Science Years 7-10 Syllabus, 2009, Sydney, Australia.

3.     Victorian Curriculum and Assessment Authority, 2013, Extended Investigation Study Design, Melbourne, Australia.

4.     Basic Electronics Tutorials Site by Wayne Storr, September 9th 2014, http://www.electronics-tutorials.ws/combination/comb_7.html

5.     Seth Lloyd, Programming the Universe – A Quantum computer scientist takes on the cosmos, 2005, Vintage Books, London, England.

6.     Alastair I.M.Rae, Beginners guide - Quantum Physics, 2005, Oneworld Publications, Oxford, England.

7.     Roger Muncaster, Relativity and Quantum Physics, 1995, Stanley Thornes Ltd, Gloucestershire, England.

8.     Paul Fleisher, ‘Secrets of the Universe: Relativity and Quantum Mechanics - Principles of modern physics’, 2002, Lerner Publications         Company, Minneapolis, United States of America.

9.     Max Tegmark & John Archibald Wheeler, ‘100 Years of Quantum Mysteries’, Scientifis American, February 2001, 

10.     Robert J. Marzano, Debra J. Pickering and Jane E. Pollock, Classroom Instruction that works: Research-based Strategies for Increasing Student Achievement, 2001, McREL and the Association for Supervision and Curriculum Development ASCD, United States of America

11.     Robert J. Marzano, The Art and Science of Teaching: A comprehensive Framework for Effective Instruction, 2007, Association for Supervision and Curriculum Development ASCD, United States of America