Information Physics: Physics-Information and Quantum Analogies for Complex Systems Modeling

By Miroslav Svitek

Information Physics: Physics-Information and Quantum Analogies for Complex Modeling presents a new theory of complex systems that uses analogy across various aspects of physics, including electronics, magnetic circuits and quantum mechanics. The book explains the quantum approach to system theory that can be understood as an extension of classical system models. The main idea is that in many complex systems there are incomplete pieces of overlapping information that must be strung together to find the most consistent model. This incomplete information can be understood as a set of non-exclusive observer results. Because they are non-exclusive, each observer registers different pictures of reality.

• Provides readers with an understanding of the analogies between very sophisticated theories of electrical circuits and currently underdeveloped information circuits, including capturing positive and negative links, as well as serial and parallel ordering of information blocks
• Integrates coverage of quantum models of complex systems using wave probabilistic functions which extend the classical probability description by phase parameters that allow researchers to model such properties as entanglement, superposition and others
• Provides readers with illustrative examples of how to use the presented theories of complex systems in specific cases such as hierarchical systems, cooperation of a team of experts, the lifecycle of the company, and the link between short and long-term memory

• Language: English
• Publisher: Academic Press
• Release date: Jun 5, 2021
• ISBN: 9780323910125

Miroslav Svitek

Dr. Miroslav Svitek is a full professor in Engineering Informatics at Faculty of Transportation Sciences, Czech Technical University in Prague. He has been the Dean of Faculty of Transportation Sciences, Czech Technical University. Since 2018, he has been a Visiting Professor in Smart Cities at University of Texas at El Paso, USA. The focus of his research includes complex system sciences and their practical applications to Intelligent Transport Systems, Smart Cities and Smart Regions. He is the author or co-author of more than 200 scientific papers and 10 books, including Quantum System Theory: Principles and Applications and Stochastic Processes: Estimation, Optimisation, and Analysis. He received his Ph.D. in radioelectronics at Faculty of Electrical Engineering, Czech Technical University. In 2005, he was been nominated as the extraordinary professor in applied informatics at Faculty of Natural Sciences, University of Matej Bel in Banska Bystrica, Slovak Republic. In 2008, Dr. Svítek was the first president of the Czech Smart City Cluster, and he is a member of the Engineering Academy of the Czech Republic. In 2006 – 2018, he served as President of the Association of Transport Telematics

Book preview

Information Physics

Physics-Information and Quantum Analogies for Complex Systems Modeling

Miroslav Svítek

Czech Technical University, Prague, Czech Republic

Matej Bel University, Banska Bystrica, Slovakia

Table of Contents

Cover image

Title page

Copyright

About the author

Preface

Acknowledgment

1. Introduction to information physics

Abstract

1.1 Dynamical system

1.2 Information representation

1.3 Information source and recipient

1.4 Information gate

1.5 Information perception

1.6 Information scenarios

1.7 Information channel

2. Classical physics–information analogies

Abstract

2.1 Electrics–information analogies

2.2 Magnetic–information analogies

2.3 Information elements

2.4 Extended information elements

2.5 Information mem-elements

3. Information circuits

Abstract

3.1 Telematics

3.2 Brain adaptive resonance

3.3 Knowledge cycle

4. Quantum physics–information analogies

Abstract

4.1 Quantum events

4.2 Quantum objects

4.3 Two (non-)exclusive observers

4.4 Composition of quantum objects

4.5 Mixture of partial quantum information

4.6 Time-varying quantum objects

4.7 Quantum information coding and decoding

4.8 Quantum data flow rate

4.9 Holographic approach to phase parameters

4.10 Two (non-)distinguished quantum subsystems

4.11 Quantum information gate

4.12 Quantum learning

5. Features of quantum information

Abstract

5.1 Quantization

5.2 Quantum entanglement

5.3 Quantum environment

5.4 Quantum identity

5.5 Quantum self-organization

5.6 Quantum interference

5.7 Distance between wave components

5.8 Interaction’s speed between wave components

5.9 Component strength

5.10 Quantum node

6. Composition rules of quantum subsystems

Abstract

6.1 Connected subsystems

6.2 Disconnected subsystems

6.3 Coexisted subsystems

6.4 Symmetrically disconnected subsystems

6.5 Symmetrically competing subsystems

6.6 Interactions with an environment

6.7 Illustrative examples

7. Applicability of quantum models

Abstract

7.1 Quantum processes

7.2 Quantum model of hierarchical networks

7.3 Time-varying quantum systems

7.4 Quantum information gyrator

7.5 Quantum transfer functions

8. Extended quantum models

Abstract

8.1 Ordering models

8.2 Incremental models

8.3 Inserted models

8.4 Intersectional extended models

9. Complex adaptive systems

Abstract

9.1 Basic agent of smart services

9.2 Smart resilient cities

9.3 Intelligent transport systems

9.4 Ontology and multiagent technologies

10. Conclusion

Appendix A. Mathematical supplement

A1 Schrodinger wave function

A2 Bohmian interpretation of wave functions

A3 Gnostic theory

A4 Heisenberg’s uncertainty limit

A5 Wave multimodels theorem

A6 Conditional mixture of quantum subsystems

A7 Information bosons, fermions, and quarks

A8 Pure and mixed wave probabilistic states

A9 Performance parameters

A10 M from N filtering

Bibliography

Index

Copyright

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Notices

Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary.

Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility.

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About the author

Prof. Dr. Ing. Miroslav Svítek, dr.h.c., FEng., EUR ING was born in Rakovník, Czech Republic, in 1969. He graduated in radioelectronics from Czech Technical University in Prague, in 1992. In 1996 he received his Ph.D. degree in radioelectronics at Faculty of Electrical Engineering, Czech Technical University in Prague (www.fel.cvut.cz).

Since 2005, he has been nominated as the extraordinary professor in applied informatics at Faculty of Natural Sciences, Matej Bel University in Banska Bystrica, Slovak Republic (www.umb.sk). Since 2008, he has been a full professor of engineering informatics at Faculty of Transportation Sciences, Czech Technical University in Prague (www.fd.cvut.cz).

In 2010–18 he was the Dean of Faculty of Transportation Sciences, Czech Technical University in Prague. Since 2018, he has been visiting professor in smart cities at the University of Texas at El Paso, Texas, United States (www.utep.edu). In Russia, he cooperates, for example, with the Institute of Mathematical Problems of Biology in Pushchino (www.impb.ru) or Moscow Automobile and Road Construction State Technical University (www.madi.ru). He lectures at various universities overseas, for example, TU Berlin in Germany (www.tu-berlin.de), Pavlodar State University in Kazakhstan (www.psu.kz), and Universidad Autónoma de Bucaramanga in Colombia (www.unab.edu.co).

The focus of his research includes quantum system theory, complex systems, and their possible applications to Intelligent Transport Systems, Smart Cities, and Smart Regions. He is the author or coauthor of more than 200 scientific papers and 10 books.

Miroslav Svítek was historically the first president of the Czech Smart City Cluster (www.czechsmartcitycluster.cz), the member of the Engineering Academy of the Czech Republic (www.eacr.cz), and in 2006–18 he was the president of the Association of Transport Telematics (www.sdt.cz).

He received the following awards:

• 2019—The Personality of Smart City, Ministry of Regional Development, Czech Republic

• 2017—Medal of CTU Prague on 310th anniversary of CTU Prague

• 2015—Silver medal of Department of Flight Transport, CTU Prague

• 2013—Medal of Institute of Technical and Experimental Physics, CTU Prague

• 2013—Gold medal of Flight Faculty, Technical University in Kosice, Slovakia

• 2010—First-grade Rector’s Award for best publication in 2009

• 2010—Silver medal of Matej Bel University, Banska Bystrica, Slovakia

• 2008—Gold Felber medal for CTU advancement

Parallel to his studies at the Czech Technical University in Prague, he attended the Prague Conservatory and graduated in the art of playing the accordion. On the occasion of the 20th anniversary of the Faculty of Transportation Sciences of the Czech Technical University in Prague, he recorded a solo album Acordeón Encantador. On the occasion of the 25th anniversary of the Faculty of Transportation Sciences, he formed a band called the Duo Profesores with Professor Ondřej Přibyl (cello) and recorded several pieces by the Argentinian composer Astor Piazzolla.

Preface

This monograph covers the work done by the author throughout the past 15 years. This work has been inspired by practical projects where large and complex systems have been modeled and dimensionality problems first identified.

The presented work should not be treated as a finished project but as the beginning of a journey. It is easy to understand that a lot of the theoretical approaches mentioned should continue and be tested in practical applications. From my point of view, the wave probabilistic functions used for a large-scale system representation seem to be a very promising area for further research because the mysteries of quantum mechanics, such as entanglement, quantization, and massive parallelism, could be used in probability theory and could enlarge current approaches into information physics and yield a better understanding of self-organization and modeling of emotion or wisdom.

The author wish all of his followers much success along the way with all of their exciting thoughts and practical experiences.

Acknowledgment

This work was partly supported by the Czech Project AI&Reasoning CZ.02.1.01/0.0/0.0/15_003/0000466 and the European Regional Development Fund.

I thank my colleagues and close friends from the Faculty of Transportation Sciences of the Czech Technical University in Prague—Prof. Miroslav Vlček, Prof. Petr Moos, Prof. Zdeněk Votruba, Prof. Mirko Novák, and Prof. Vladimír Mařík. They acted as conveners of my first steps into the world of system science and revealed new methods in this field to me. It was their examples, which taught me to aspire to a research career. Let me also thank the Faculty of Natural Sciences of the Matej Bel University in Banska Bystrica, Slovak Republic, for their support and the friendly creative environment.

This work was partly written during my sabbatical leave at the College of Engineering, The University of Texas at El Paso (UTEP). I take this opportunity to thank Dr. Carlos Ferregut and Dr. Kelvin Cheu for their support during my stay at UTEP. Last but not least, my thanks also belong to Dr. Tomas Horak and Donald Griffin for the perfect language correction of the text.

Finally, I take this opportunity to thank my wife, my whole family, and my friends for their patience and tolerance during the process of my work. Last but not least, I dedicate my special thanks to my daughter Kamilka and son Martinek for their everlasting inspiration.

1

Introduction to information physics

Abstract

The chapter introduces the basic quantities of information physics such as information flow and information content. Using these quantities, information gates and other more complex elements are introduced. These approaches are shown on several examples, such as modeling the link between the source and the recipient of information.

Keywords

Information representation; information gate; information perception; information scenarios; information channel

Imagine the information on the example of building a house. We need material (or mass), as well as plenty of workers (or energy), but without the knowledge of the plans as for when and how to build, we cannot erect the house. Information and knowledge are, therefore, the things that enrich the complex system theory and afterwards also natural sciences, enabling them to describe more faithfully the world around us.

Information was interestingly described by George Bernard Shaw: If you have an apple and I have an apple, and we exchange apples, we both still only have one apple. But if you have an idea (a piece of information) and I have an idea, and exchange ideas (this information), we each now have two ideas (two pieces of information).

1.1 Dynamical system

Dynamical system can be defined as a specific information model of a part of the world (object) that interacts with its environment, for example, through several inputs and outputs. This model is isomorphic with the object considering a selected set of criteria.

The state space system description is understood to transform an input vector into a state space vector, whereas an output vector can be found through the transformation of input and state vectors into an output vector. By an input–output system description, the relation between input and output vectors is understood. The dynamical system from this point of view is, therefore, understood to be as a black box whose characteristics can be identified through a system response to the input vector.

Let describe the discrete dynamical system response to the input signal. Dirac impuls is defined as sequence {1,0,0,0,..}, is defined as shifted sequence {0,1,0,0,..}, and means m-shifted Dirac impulse. The Dirac impulse can be used as an input signal, and the system response to the Dirac impulse can be given as:

(1.1)

where is the impulse response to the shifted Dirac impulse .

In system theory, the input series can be expressed by a sequence of Dirac impulses:

(1.2)

We can easily add the input series (1.2) into the system description and determine the output series as follows:

(1.3)

A discrete dynamical system is defined as linear if the following equation holds:

(1.4)

where are the output responses of a system to the inputs :

(1.5)

When speaking of time invariant systems it means that all events are dependent only on the event time difference (n−m) and not on the event in time n or m, respectively.

Time invariant dynamical system descriptions can be defined through the convolution sum:

(1.6)

The system is causal if the output signal is dependent only on current and past input values , and