Quantum Computing (Chalmers University)

 

Lecture details

This lecture will be held by Professor Di Vincenzo.

Description:

  • Elementary quantum gates and basic quantum computing formalism,
  • Introduction to complexity classes and relevant conjectures,
  • Circuit model for quantum computation,
  • Foundational theorems for quantum computation: Solovey Kitaev theorem; Gottesman-Knill theorem,
  • Other models for universal quantum computation beyond the circuit model: Measure-ment Based Quantum Computation and Adiabatic quantum computation,
  • Quantum Fourier Transform and Phase estimation algorithms,
  • Shor’s algorithm,
  • Quantum Machine Learning,
  • Quantum Cloud Computing exercise,
  • Quantum algorithms for solving combinatorial optimization problems: quantum anneal-ing and QAOA,
  • Variational quantum eigensolver,
  • Quantum superiority models: Boson sampling and the instantaneous quantum polyno-mial (IQP) protocol,
  • Continuous-Variable (CV) quantum computation: MBQC and GKP encoding,
  • CV Quantum superiority models: CV IQP,
  • CV annealing

Prerequisites:

It is recommended that the students have taken either "Quantum optics and quantum information", "Quantum mechanics", or some other equivalent course.

Learning goal:

The students learn modern relevant quantum algorithms and their purposes. The stu-dents understand the key principles of the various models of quantum computation (circuit, measurement-based, adiabatic model).
The students obtain the basic structure of the quantum algorithms addressed in the course that are based on the circuit model, and to compute the outcome of basic quan-tum circuits.
The students compare, in terms of time complexity, what quantum advantage is ex-pected from the quantum algorithms addressed in the course with respect to their clas-sical counterparts.
The students program simple quantum algorithms on a cloud quantum computer or a cloud simulator.
The students acquire understanding of the basic principles of the continuous variable encoding for quantum information processing.
The students obtain examples of the motivation for applying quantum computing to machine learning and of what the obstacles are to achieving an advantage from doing so.

The course comprises lectures, tutorial exercise sessions, and a programming laboratory exercise.

Literature:

Michael A. Nielsen and Isaac L. Chuang, Quantum Information and Quantum Computation, Cambridge University Press, 2000

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