Nicolas Macris

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Senior Scientist +41 21 693 81 14

INR 134 (Bâtiment INR)
Station 14
CH-1015 Lausanne

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Administrative data


Selected publications

Other publications


Teaching & PhD


Communication Systems

Computer Science

PhD Programs

Doctoral program in computer and communication sciences


Quantum information processing

Information is stored and processed in material systems. With their miniaturization, it becomes necessary to replace the concept of classical bit by that of quantum bit. This course develops the subject of quantum communications, cryptography and correlations. The IBM Q machine will be discussed.

Markov chains and algorithmic applications

The study of random walks finds many applications in computer science and communications. The goal of the course is to get familiar with the theory of random walks, and to get an overview of some applications of this theory to problems of interest in communications, computer and network science.

Quantum Information Theory and Computation

Today one is able to manipulate matter at the nanoscale were quantum behavior becomes important and possibly information processing will have to take into account laws of quantum physics. We introduce concepts developed in the last 25 years to take advantage of quantum resources.

Statistical Physics for Communication and Computer Science

The course introduces the student to notions of statistical physics which have found applications in communications and computer science. We focus on graphical models with the emergence of phase transitions, and their relation to the behavior of efficient algorithms.

Quantum computation

The miniatursation of computing devices leads to revise the classical concepts of computation and develop models of quantum computation. The course introduces quantum bits, elementary quantum logical gates and circuits, the main quantum algorithms. We also introduce the students to the IBM Q machine

Learning theory

Machine learning and data analysis are becoming increasingly central in many sciences and applications. This course concentrates on the theoretical underpinnings of machine learning.