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227-0162-00L 4 Credits MSC D-ITET

Integrated Quantum, Statistical, and Information Mechanics for Information Processing

Lecturers & Examiners: Prof. Dr. Sandip Tiwari
Does not take place this semester.
VVZ CR n/a

Last Updated: 2026-02-05 15:40:48

Abstract

Computing with devices as physical objects composed of atoms, electrons and photons is an architected assembly where information is grounded in and transformed in quantum, statistical and information mechanics. This course is an integrated introduction to quantum, statistical, and information mechanics bringing out the common principles of these subjects with an engineering emphasis.

Objective

This course is an integrated introduction to information-centered foundational ideas to build an understanding of quantum, statistical and information mechanics as applied generally in engineering and specifically in computing. Computing employs hardware and objective manipulation of signals turned into data to access desired information. A logical state of a computer must be represented as a physical state in the hardware. Devices as physical objects have the information grounded in quantum, statistical, and information mechanics. These three science and engineering specialties are our theories for describing and predicting the abstracted behavior. The power dissipation in signal and data manipulation, the speed with which changes happen, the various architectures through which one may affect the change, the error rates, etc. are all grounded in the information that underscores what we practice in quantum mechanics, statistics, solid-state, electronics and information theory. In the quantum approach, information gained arises in the observation. In the statistical approach, it is through the probabilistic extractions. Preservation of information content in the presence of noise and fluctuations leads to dissipation. Inferencing and computation requires information compression through the objective symbolic manipulation and efficient coding. Entanglement and entropy are intrinsic in this probabilistic edifice where the inferences are drawn. Deterministic computing, as in the traditional approach, and non-deterministic computing such as the modern machines learning using neural networks, depend on how the information is manipulated in the midst of this entropy and entanglement. In addition to the introductory integrative understanding of quantum, statistical, and information mechanics, this course helps an understanding of the character of information processing through the traditional computing, quantum computing and neural network techniques.

Content

An introduction to the basic tenets of quantum mechanics (axioms, operators, observation, perturbation, evolution, mixed states), statistical mechanics (basics of thermodynamics, principles of statistical mechanics, particle statistics, entropy, classical-to-quantum), information mechanics (various entropies, mutual information, data and channels, Bayesian approach, Fisher information, maximum entropy) and their use in exploration of computing via different architectures of computation (BLAS, von Neumann and neural) together with an introductory understanding of quantum computation. This integrated understanding emphasizes physical insights together with a mathematical development.

Resources

Lecture Notes

Lecture material and scripts

General Information

Language
English
Levels
MSC

Examination

Type
graded semester performance

Course Components

Type Title Time & Place Hours
lecture Integrated Quantum, Statistical, and Information Mechanics for Information Processing
Does not take place this semester.
No time listed 2 h weekly
exercise Integrated Quantum, Statistical, and Information Mechanics for Information Processing
Does not take place this semester.
No time listed 2 h weekly

Offered In