Training
The training consists of a 4-year cohort-based PhD programme with integrated studies.
In their first year, all students are located together at the University of Edinburgh.
At the end of year 1, students are matched with a project and supervisory team, and relocate to their primary supervisor’s institution.
Core Courses
The first year has several core components.
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Introduction to Quantum Computing
This taught course teaches all the basics of quantum computing, including the standard algorithms, measurement-based quantum computing, the fundamentals of error correction, and practical quantum programming.
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Practical Orientation
This course includes personal development modules by the Institute for Academic Development on research planning and management, writing and publishing, personal effectiveness, and research ethics and integrity.
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Residency at the National Quantum Computing Centre
Students spend 2 weeks at the premises of the National Quantum Computing Centre. Sandbox sessions introduce various quantum hardware platforms using the relevant software development kits. A Responsible Research and Innovation analysis surveys approaches to quantum technologies including regulation.
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Group Project
This seminar-style course teams students into small groups of students from different undergraduate backgrounds. Students read, discuss, and present research from various parts of the literature. This leads to a small research project in teams.
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Topics in Quantum Informatics
This course is designed to overview the research topics. They are introduced by the 70+ experts and supervisors in the form of an introductory seminar, to facilitate the PhD topic selection.
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Individual Project
Each student undertakes an individual research project, mentored by an academic supervisor and possibly a supervisor from an industry partner. This develops a coherent research programme for years 2-4.
Elective Courses
Quantum Informatics is a truly interdisciplinary subject. Elective courses round out background knowledge. For example, physicists could consider programming courses, and mathematicians could consider basic engineering or quantum mechanics courses. Options include:
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Introduction to Quantum Programming and Semantics
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Quantum Cyber Security
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HPC architectures
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Design and Analysis of Parallel Algorithms
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Accelerated Systems: Principles and Practice
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Elements of Programming Languages
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Computer Architecture and Design
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Introduction to Modern Cryptography
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Computational Complexity
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Algorithms and Data Structures
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Randomised Algorithms
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Machine Learning
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Topics in Mathematical Physics
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Quantum Information
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Fundamentals of Optimization
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Category Theory
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Quantum Field Theory
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Quantum Theory
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Symmetries of Quantum Mechanics
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Advanced Coding Techniques
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Machine Learning in Signal Processing
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Digital Communication Fundamentals
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Probability, Estimation Theory and Random Signals
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Python Programming
Other Activities
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Spring Schools
Each cohort organises a week-long spring graduate school in their second year, at one of the host institutions or elsewhere. Students select the topic and invite (external) lecturers.
To maximise reach, the spring school is open to non-CDT students and may take the form of a joint conference.
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Residential Symposia
During annual off-site residential symposia, all cohorts mix and learn about each other’s research projects. Non-technical topics are also discussed, including well-being, fairness, and work-life balance.
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Peer-to-peer Learning Seminars
Regular reading groups keep students up to date on appropriate literature or skills, with input and feedback from academic or industry leaders on the topic.
Industry Placements and Research Visits
Every student is strongly encouraged to spend 1-3 months of their research with partners. This can be a placement or internship at one of our 30 industry partners to experience application discovery and development:
Aegiq
Algorithmiq
AliceBob
Atom Computing
AWE
AWS
BT
Cambridge Consultants
Entropica Labs
Google
Hartree Centre
IBM
Infleqtion
IOG
IOG
IonQ
IQM
National Physical Laboratory
National Quantum Computing Centre
Nu Quantum
OQC
ORCA Computing
Pasqal
Phasecraft
QBA
Quandela
Quantinuum
QuiX Quantum
Rigetti
Riverlane
SandboxAQ
SEEQC
VeriQloud
Xanadu
This can also be an immersive research visit to one of our 11 international partner universities to experience starting collaborations and developing research proposals:
- Lawrence National Laboratory, UC Berkeley
- University of Tokyo
- Sydney Quantum Academy
- Institute for Quantum Computing, University of Waterloo
- Center for Quantum Information and Computer Science, University of Maryland
- Quantum National Hub, University of Leiden
- Quantum Hub, University of Copenhagen
- QuSoft, University of Amsterdam
- Quantum Information Center, Sorbonne University Paris
Entrepreneurship
This optional programme, where students build the skills to commercialise their work, includes mentoring, access to business advisors and a network of investors, researchers, peers, and industry.
For Scotland-based students this is provided by Edinburgh Innovations’ Venture Builder Incubator, which has accelerated 280 tech ventures, and helped 150 founding teams raise £30m in 2022.
For England-based students this training is provided by UCL’s ConceptionX, who helped 360 founding teams raise over £63m since 2018.
Responsible Research and Innovation
This bespoke intensive course consists of a foundation module, and conversation-based supplemental training.
Developed by established leaders in Responsible Research and Innovation in Quantum Informatics such as Dr. Michael Barany, Prof. Marina Jirotka, and Prof. Joyce Tait, it explores the impacts of Quantum Informatics on society and vice versa.
Cohort connections
Students of a cohort go through the training programme together, interacting intensively in the first year, and regularly reinforcing their connection in later years. Graduate cohorts combine into a strong network of future Quantum Informatics leaders in the UK.
Students also learn across cohorts, with later-year student mentors looking after incoming cohort students, and tutoring taught courses in their subject.