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CSC3133

Quantum Algorithms

  • Offered for Year: 2025/26
  • Module Leader(s): Dr Jonte Hance
  • Owning School: School of Computing
  • Teaching Location: Newcastle City Campus

Semesters

Your programme is made up of credits, the total differs on programme to programme.

Semester 2 Credit Value: 10
ECTS Credits: 5.0

Aims

Quantum computing is rapidly developing as one of the most important areas in computing – however, the algorithms which we hope to deploy on quantum computers, both now on noisy intermediate-scale quantum (NISQ) devices, and in future on error-corrected “ideal” systems, require both an understanding of the formalism of quantum information, and an understanding of key properties of quantum systems which such algorithms then make use of (e.g., superposition, entanglement, contextuality). In this course, building on the tools provided by CSC3132, we introduce key quantum algorithms (both for NISQ and error-corrected systems), and dive “under the hood” to look a bit more at the features which seem to power the “quantum advantage” these quantum algorithms should give over classical algorithms aiming to solve the same problem.

Outline Of Syllabus

  • Review of the quantum formalism and quantum circuits
  • Basic Quantum Algorithms (e.g., Deutsch-Jozsa)
  • Grover’s Algorithm
  • Quantum Fourier Transform
  • Shor’s algorithms
  • Approximation of general unitaries (e.g., Solovay-Kitaev theorem)
  • Hybrid Quantum-Classical (e.g., Variational Quantum Algorithms)
  • Quantum Annealing/Simulation
  • Contextuality and Quantum Advantage
  • Quantum Computing in Practice

Learning Outcomes

Intended Knowledge Outcomes

By the end of the module students should be aware of and understand:

  • the main differences between classical and quantum computers
  • the quantum circuit notation
  • the main quantum algorithms and protocols
  • the different technologies for hardware implementation

Intended Skill Outcomes

By the end of the module, students should be able to:

  • write and execute nontrivial quantum algorithms using quantum circuit simulators and computers
  • design and implement quantum algorithms
  • analyse the output of quantum simulations
  • implement hybrid classical/quantum algorithms

Teaching Methods

Teaching Activities

CategoryActivityNumberLengthStudent HoursComment
Scheduled Learning And Teaching Activities Lecture 10 1:00 10:00 Lectures (Present in-person)
Scheduled Learning And Teaching Activities Practical 5 2:00 10:00 Practical sessions using tools (e.g., Qiskit, Pennylane) to write and implement quantum algorithms.
Guided Independent Study Assessment preparation and completion 10 1:00 10:00 Complete weekly online problems.
Guided Independent Study Assessment preparation and completion 1 20:00 20:00 Preparation for final examination.
Guided Independent Study Independent study 5 2:00 10:00 Background reading.
Guided Independent Study Independent study 10 3:00 30:00 Revise lecture materials.
Guided Independent Study Independent study 5 2:00 10:00 Complete lab practicals.
Total       100:00  

Teaching Rationale And Relationship

The lectures convey the key theoretical concepts, algorithms, and illustrative examples that will be tried and extended upon in the lab. Quantum computing is quite counterintuitive, and therefore a solid understanding of its theory is required – this cannot be learnt by experimentation. The computer practicals give students hands-on experience with quantum circuits run on simulators or quantum computers, to reinforce the theoretical concepts delivered in the lectures.

Assessment Methods

The format of resits will be determined by the Board of Examiners.

Exams

ComponentLength (mins)SemesterWhen setPercentageComment
Written Examination 1 90 2 A 60 N/A

Other Assessments

ComponentSemesterWhen setPercentageComment
Problem solving exercises 1 2 M 4 Weekly online assessed question set 1
Problem solving exercises 2 2 M 4 Weekly online assessed question set 2
Problem solving exercises 3 2 M 4 Weekly online assessed question set 3
Problem solving exercises 4 2 M 4 Weekly online assessed question set 4
Problem solving exercises 5 2 M 4 Weekly online assessed question set 5
Problem solving exercises 6 2 M 4 Weekly online assessed question set 6
Problem solving exercises 7 2 M 4 Weekly online assessed question set 7
Problem solving exercises 8 2 M 4 Weekly online assessed question set 8
Problem solving exercises 9 2 M 4 Weekly online assessed question set 9
Problem solving exercises 10 2 M 4 Weekly online assessed question set 10

Assessment Rationale And Relationship

The main aim of the written exam is to assess the student’s understanding of the theory of quantum computing and quantum algorithms delivered through the lectures – this is necessary to measure to what extent the subject is mastered. The ten weekly problem sets focus on the student’s ability to apply theory to practice and solve simple problems on the quantum algorithms presented at lectures.

Timetable