COMP SCI 4813 - Introduction to Quantum Computing

North Terrace Campus - Semester 2 - 2019

It is still unknown whether quantum computers will ever be a reality. If such machines are possible, they will fundamentally change how we perform calculations, and the implications on many applications (including communications and computer security) will be tremendous. Leaving the issue of feasibility aside, it is interesting nonetheless to study how to do computing using a quantum computer. This course aims to provide a first introduction to quantum computing. We will highlight the paradigm change between conventional computing and quantum computing, and introduce several basic quantum algorithms. If time permits, we will also discuss the implications of quantum computing on fields such as computer security and machine learning.

  • General Course Information
    Course Details
    Course Code COMP SCI 4813
    Course Introduction to Quantum Computing
    Coordinating Unit School of Computer Science
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week
    Available for Study Abroad and Exchange Y
    Prerequisites COMP SCI 2201
    Assumed Knowledge Linear Algebra, Alg Data Structure Analysis
    Course Description It is still unknown whether quantum computers will ever be a reality.
    If such machines are possible, they will fundamentally change how we perform calculations, and the implications on many applications (including communications and computer security) will be tremendous.
    Leaving the issue of feasibility aside, it is interesting nonetheless to study how to do computing using a quantum computer. This course aims to provide a first introduction to quantum computing. We will highlight the paradigm change between conventional computing and quantum computing, and introduce several basic quantum algorithms. If time permits, we will also discuss the implications of quantum computing on fields such as computer security and machine learning.
    Course Staff

    Course Coordinator: Dr Tat-Jun Chin

    • Prof Frank Neumann
    • Dr Yasir Latif
    • Dr Michele Sasdelli
    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    On successful completion, students will gain understanding of:
    1. The basic principles of quantum computing.
    2. The fundamental differences between conventional computing and quantum computing.
    3. Several basic quantum computing algorithms.
    4. The classes of problems that can be expected to be solved well by quantum computers.
    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)
    Deep discipline knowledge
    • informed and infused by cutting edge research, scaffolded throughout their program of studies
    • acquired from personal interaction with research active educators, from year 1
    • accredited or validated against national or international standards (for relevant programs)
    1,2,3,4
    Critical thinking and problem solving
    • steeped in research methods and rigor
    • based on empirical evidence and the scientific approach to knowledge development
    • demonstrated through appropriate and relevant assessment
    1,2,3,4
    Career and leadership readiness
    • technology savvy
    • professional and, where relevant, fully accredited
    • forward thinking and well informed
    • tested and validated by work based experiences
    1,2,3,4
  • Learning Resources
    Required Resources
    Students must be enrolled in the course on MyUni to obtain course material and receive important announcments.
    Recommended Resources
    The main textbook used in the course is Quantum Computing: A Gentle Introduction, by Eleanor Rieffel and Wolfgang Polak (available from the university library).
  • Learning & Teaching Activities
    Learning & Teaching Modes
    The course will be conducted via a series of lectures with discussions and a guest lecture.
    Workload

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    There will be 4 assignments (written and/or programming exercises) and a final exam.
    Learning Activities Summary

    No information currently available.

  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary

    There will be 4 assignments weighted at 10% each, and a final exam weighted at 60%.

    There is a minimum performance hurdle of 40% placed on the final exam component.

    Assessment Detail

    No information currently available.

    Submission

    No information currently available.

    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M11 (Honours Mark Scheme)
    GradeGrade reflects following criteria for allocation of gradeReported on Official Transcript
    Fail A mark between 1-49 F
    Third Class A mark between 50-59 3
    Second Class Div B A mark between 60-69 2B
    Second Class Div A A mark between 70-79 2A
    First Class A mark between 80-100 1
    Result Pending An interim result RP
    Continuing Continuing CN

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

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