2019 Quantum Information

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Academic unit or major
Graduate major in Physics
Instructor(s)
Tilma Todd 
Course component(s)
Lecture
Day/Period(Room No.)
Tue3-4(H115)  Fri3-4(H115)  
Group
-
Course number
PHY.Q435
Credits
2
Academic year
2019
Offered quarter
4Q
Syllabus updated
2019/3/18
Lecture notes updated
-
Language used
English
Access Index

Course description and aims

This is a six-part graduate-level introduction to quantum information and its associated technologies. One third of the class is devoted to discussions on the theoretical underpinnings of quantum information; one-third to discussions on the various physical realizations of said theory; and one-third to experimental work on the IBM Q Experience. At the end of this course, students will have a general understanding of the current state-of-the-art of quantum information science that any theorist, or experimentalist wanting to be in the field of quantum information technology, should know.

Student learning outcomes

By the end of this class students are expected to be able to understand and make use of the differences between classical and quantum information theory; understand the various physical systems used for quantum information technologies and their limitations; be able to design and implement various quantum circuits on both simulators and real processors; and be able to exploit available and forthcoming quantum information technology products for their own research.

Keywords

Quantum Information Science, Quantum Computer (Theory), Quantum Computer (Experiment), Quantum Algorithms, Quantum Error Correction, Quantum Simulation

Competencies that will be developed

Intercultural skills Communication skills Specialist skills Critical thinking skills Practical and/or problem-solving skills

Class flow

This class is divided into six, three-week parts; each part focused on understanding various key aspects of quantum information science and technology. By the end of the class, students should be able to understand the basic science and engineering concepts behind current quantum information technologies.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction to quantum mechanics and the foundations of classical computing - Theory Physical basis of information; Waves and interference; Quantum superposition and entanglement; etc.
Class 2 Introduction to quantum mechanics and the foundations of classical computing - Applications Classical vs. quantum bits; Classical vs. quantum computational complexity: etc.
Class 3 Introduction to QISkit and the IBM Q Experience Installation and overview of Python software development kit (SDK) for working with OpenQASM and the IBM Q Experience (QX); Jupyter notebooks and Anaconda integration; etc.
Class 4 Introduction to quantum information and the foundations of quantum computing - Theory Entanglement; The quantum Fourier transform and periodicity; etc.
Class 5 Introduction to quantum information and the foundations of quantum computing - Application DiVincenzo criteria; Quantum gates; etc.
Class 6 Basics of quantum gates on the IBM Q Experience (QX) Deployment of simple gates on the IBM Q Experience; etc.
Class 7 Introduction to simple quantum protocols and quantum algorithms - Theory Integer factorization; Phase estimation; etc.
Class 8 Introduction to simple quantum protocols and quantum algorithms - Application Deutsch–Jozsa algorithm; Shor’s algorithm; Grover’s algorithm; etc.
Class 9 Basics of quantum algorithms on the IBM Q Experience (QX) - Part I Deployment of simple one and two-qubit algorithms on the IBM Q Experience; etc.
Class 10 Introduction to Noisy Intermediate-Scale Quantum Computing (NISQ) - Theory Hamiltonian simulation; Quantum walks; etc.
Class 11 Introduction to Noisy Intermediate-Scale Quantum Computing (NISQ) - Application Quantum chemistry; Machine learning; etc.
Class 12 Basics of quantum algorithms on the IBM Q Experience (QX) - Part II Deployment of simple multi-qubit algorithms on the IBM Q Experience; etc.
Class 13 Introduction to noise and error correction - Theory Noise and the framework of quantum channels; Quantum error correction codes; etc.
Class 14 Introduction to noise and error correction - Application Physical phenomena as quantum bits; Large-scale quantum error correction; etc.
Class 15 General quantum algorithms on the IBM Q Experience (QX) - Part I Deployment of general multi-qubit algorithms on the IBM Q Experience; etc.
Class 16 Introduction to quantum communication and other quantum information technologies - Theory Quantum state discrimination and tomography; High-level quantum programming; etc.
Class 17 Introduction to quantum communication and other quantum information technologies - Application Quantum computing hardware and architecture; QIT industry; etc.
Class 18 General quantum algorithms on the IBM Q Experience (QX) - Part II Deployment of advanced multi-qubit algorithms on the IBM Q Experience; etc.

Textbook(s)

N. David Mermin, “Quantum Computer Science - An Introduction”
https://doi.org/10.1017/CBO9780511813870

Reference books, course materials, etc.

M. A. Nielsen and I. L. Chuang, "Quantum Computation and Quantum Information"
ISBN : 978-1107002173

As needed, appropriate course materials and references will be made available before class via Moodle (https://tilma-labs.org/moodle) and Slack (https://tt-physics-qit.slack.com/).

Assessment criteria and methods

Assessment is thru attendance, homework, and the successful execution of assigned simulations and experiments on the IBM Q Experience

Related courses

  • ZUB.Q204 : Quantum Mechanics I
  • ZUB.Q313 : Quantum Mechanics III
  • MCS.T204 : Introduction to Computer Science
  • ICT.C601 : Quantum Information Processing

Prerequisites (i.e., required knowledge, skills, courses, etc.)

There are no prerequisites.

Other

Before coming to class, students should read the course schedule and check what topics will be covered. Required learning should be completed outside of the classroom for preparation and review purposes.

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