BOGOLIUBOV'S VISION: QUASIAVERAGES AND BROKEN SYMMETRY TO QUANTUM PROTECTORATE AND EMERGENCE

In the present interdisciplinary review, we focus on the applications of the symmetry principles to quantum and statistical physics in connection with some other branches of science. The profound and innovative idea of quasiaverages formulated by N. N. Bogoliubov, gives the so-called macro-objectivation of the degeneracy in the domain of quantum statistical mechanics, quantum field theory and quantum physics in general. We discuss the complementary unifying ideas of modern physics, namely: spontaneous symmetry breaking, quantum protectorate and emergence. The interrelation of the concepts of symmetry breaking, quasiaverages and quantum protectorate was analyzed in the context of quantum theory and statistical physics. The chief purposes of this paper were to demonstrate the connection and interrelation of these conceptual advances of the many-body physics and to try to show explicitly that those concepts, though different in details, have certain common features. Several problems in the field of statistical physics of complex materials and systems (e.g., the chirality of molecules) and the foundations of the microscopic theory of magnetism and superconductivity were discussed in relation to these ideas.

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Using Quantum Agent-Based Simulation to Model Social Networks

Semantic Web Personalization and Context Awareness ◽

10.4018/978-1-61520-921-7.ch005 ◽

2011 ◽

pp. 42-54

Author(s):

C. Bisconti ◽

A. Corallo ◽

M. De Maggio ◽

F. Grippa ◽

S. Totaro

Keyword(s):

Social Networks ◽

Quantum Mechanics ◽

Quantum Physics ◽

Business Processes ◽

Social Dynamics ◽

Social Systems ◽

Many Body ◽

Agent Based ◽

Microscopic Interactions ◽

The Many

This research aims to apply models extracted from the many-body quantum mechanics to describe social dynamics. It is intended to draw macroscopic characteristics of organizational communities starting from the analysis of microscopic interactions with respect to the node model. In this chapter, the authors intend to give an answer to the following question: which models of the quantum physics are suitable to represent the behaviour and the evolution of business processes? The innovative aspects of the project are related to the application of models and methods of the quantum mechanics to social systems. In order to validate the proposed mathematical model, the authors intend to define an open-source platform able to model nodes and interactions within a network, to visualize the macroscopic results through a digital representation of the social networks.

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The many-body content of quantum gauge theories and spontaneous symmetry breaking

Nuclear Physics B ◽

10.1016/0550-3213(82)90295-4 ◽

1982 ◽

Vol 197 (2) ◽

pp. 334-346 ◽

Cited By ~ 7

Author(s):

F. Palumbo

Keyword(s):

Symmetry Breaking ◽

Spontaneous Symmetry Breaking ◽

Gauge Theories ◽

Many Body ◽

The Many

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Experimentally Accessible Witnesses of Many-Body Localization

Quantum Reports ◽

10.3390/quantum1010006 ◽

2019 ◽

Vol 1 (1) ◽

pp. 50-62 ◽

Cited By ~ 1

Author(s):

Marcel Goihl ◽

Mathis Friesdorf ◽

Albert H. Werner ◽

Winton Brown ◽

Jens Eisert

Keyword(s):

Statistical Physics ◽

Optical Lattices ◽

Cold Atoms ◽

Quantum Statistical Mechanics ◽

Quantum Systems ◽

Many Body ◽

Physical Systems ◽

Tensor Network ◽

Distinct Features ◽

Flight Measurements

The phenomenon of many-body localized (MBL) systems has attracted significant interest in recent years, for its intriguing implications from a perspective of both condensed-matter and statistical physics: they are insulators even at non-zero temperature and fail to thermalize, violating expectations from quantum statistical mechanics. What is more, recent seminal experimental developments with ultra-cold atoms in optical lattices constituting analog quantum simulators have pushed many-body localized systems into the realm of physical systems that can be measured with high accuracy. In this work, we introduce experimentally accessible witnesses that directly probe distinct features of MBL, distinguishing it from its Anderson counterpart. We insist on building our toolbox from techniques available in the laboratory, including on-site addressing, super-lattices, and time-of-flight measurements, identifying witnesses based on fluctuations, density–density correlators, densities, and entanglement. We build upon the theory of out of equilibrium quantum systems, in conjunction with tensor network and exact simulations, showing the effectiveness of the tools for realistic models.

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Statistical physics of network structure and information dynamics

Journal of Physics: Complexity ◽

10.1088/2632-072x/ac457a ◽

2021 ◽

Author(s):

Arsham Ghavasieh ◽

Manlio De Domenico

Keyword(s):

Machine Learning ◽

Statistical Physics ◽

Quantum Physics ◽

Network Science ◽

Information Science ◽

Wide Spectrum ◽

Quantum Statistical Mechanics ◽

Information Dynamics ◽

Statistical Field ◽

The Rich

Abstract In the last two decades, network science has proven to be an invaluable tool for the analysis of empirical systems across a wide spectrum of disciplines, with applications to data structures admitting a representation in terms of complex networks. On the one hand, especially in the last decade, an increasing number of applications based on geometric deep learning have been developed to exploit, at the same time, the rich information content of a complex network and the learning power of deep architectures, highlighting the potential of techniques at the edge between applied math and computer science. On the other hand, studies at the edge of network science and quantum physics are gaining increasing attention, e.g., because of the potential applications to quantum networks for communications, such as the quantum Internet. In this work, we briefly review a novel framework grounded on statistical physics and techniques inspired by quantum statistical mechanics which have been successfully used for the analysis of a variety of complex systems. The advantage of this framework is that it allows one to define a set of information-theoretic tools which find widely used counterparts in machine learning and quantum information science, while providing a grounded physical interpretation in terms of a statistical field theory of information dynamics. We discuss the most salient theoretical features of this framework and selected applications to protein-protein interaction networks, neuronal systems, social and transportation networks, as well as potential novel applications for quantum network science and machine learning.

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Using Quantum Agent-Based Simulation to Model Social Networks

Nanotechnology ◽

10.4018/978-1-4666-5125-8.ch040 ◽

2014 ◽

pp. 909-921

Author(s):

C. Bisconti ◽

A. Corallo ◽

M. De Maggio ◽

F. Grippa ◽

S. Totaro

Keyword(s):

Social Networks ◽

Quantum Mechanics ◽

Quantum Physics ◽

Business Processes ◽

Social Dynamics ◽

Social Systems ◽

Many Body ◽

Agent Based ◽

Microscopic Interactions ◽

The Many

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Quantum Modeling of Social Dynamics

International Journal of Knowledge Society Research ◽

10.4018/jksr.2010010101 ◽

2010 ◽

Vol 1 (1) ◽

pp. 1-11 ◽

Cited By ~ 5

Author(s):

C. Bisconti ◽

A. Corallo ◽

M. De Maggio ◽

F. Grippa ◽

S. Totaro

Keyword(s):

Knowledge Production ◽

Quantum Physics ◽

Social Dynamics ◽

Mathematic Model ◽

Work Team ◽

Many Body ◽

Innovation Potential ◽

The Social ◽

The Many ◽

Quantum Modeling

In this paper, the authors apply models extracted from the Many-Body Quantum Mechanics to understand how knowledge production is correlated to the innovation potential of a work team. This study is grounded in key assumtpions. First, complexity theory applied to social science suggests that it is of paramount importance to consider elements of non-objectivity and non-determinism in the statistical description of socio-economic phenomena. Second, a typical factor of indeterminacy in the explanation of these phenomena lead to the need to apply the instruments of quantum physics to formally describe social behaviours. In order to experiment the validity of the proposed mathematic model, the research intends to: 1) model nodes and interactions; 2) simulate the network behaviour starting from specific defined models; 3) visualize the macroscopic results emerging during the analysis/simulation phases through a digital representation of the social network.

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Approximating Ground States by Neural Network Quantum States

10.20944/preprints201812.0218.v1 ◽

2018 ◽

Author(s):

Ying Yang ◽

Chengyang Zhang ◽

Huaixin Cao

Keyword(s):

Neural Network ◽

Relative Error ◽

Ground State ◽

Quantum Physics ◽

Body Problem ◽

Body Wave ◽

Quantum States ◽

Many Body ◽

The Many ◽

Exponential Complexity

The many-body problem in quantum physics originates from the difficulty of describing the non-trivial correlations encoded in the exponential complexity of the many-body wave function. Motivated by the Giuseppe Carleo's work titled solving the quantum many-body problem with artificial neural networks [Science, 2017, 355: 602], we focus on finding the NNQS approximation of the unknown ground state of a given Hamiltonian $H$ in terms of the best relative error and explore the influences of sum, tensor product, local unitary of Hamiltonians on the best relative error. Besides, we illustrate our method with some examples.

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