TIES4525 Quantum Algorithms (5 op)

Throughout this course, we will explore prevalent methodologies in quantum algorithm design along with key quantum algorithms that have been developed thus far. Additionally, we will delve into intercon-nected aspects of quantum computing that bear relevance to the realm of quantum algorithms.

Upon completion of this course, students understand: 

• The fundamental principles underlying quantum algorithms. 
• How quantum algorithms work. 
• How to apply quantum algorithms. 
• How to analyze and implement key quantum algorithms. 
• How to develop Python code relevant to quantum algorithms.

Basic knowledge of mathematics, linear algebra, statistics, complex numbers, algorithm data structure analysis, and logic circuit design will be helpful but not necessary. 

Passing the course on "Quantum Computing Essentials (Kvanttilasken-nan aakkoset)," or a similar course is recommended as prior knowledge, but it is not mandatory. 

Primary audiences are M.Sc. students in information science, computer science, and electrical engineering. 

B.Sc. students in information science, computer science, and electrical engineering may participate if they have a basic understanding of the prerequisites.

Study Materials: 

• Lecture slides 
• Sample Python programs 
• Journal Papers. 

Literature: 

• Lipton, R. J., & Regan, K. W. (2021). Introduction to quantum algorithms via linear algebra. MIT Press. 
• Quantum computing explained, by David McMahon, John Wiley & Sons, 2007. 
• Quantum Computing: A Gentle Introduction, by Eleanor Rieffel and Wolfgang Polak, MIT Press, 2011. 
• Quantum Computer Science: An Introduction, by N. David Mermin, Cambridge University Press, 2007. 
• Quantum computing: From linear algebra to physical realiza-tions, by M. Nakahara and T. Ohmi, CRC Press, 2008. 
• Nielsen &Chuang, Quantum Computing and Quantum Infor-mation, Cambridge University Press, 10th Anniversary Edition, 2010.