href="#litres_trial_promo">Part 4 develops the smart network field theory on the basis of statistical physics, information theory, and model field theory systems. Chapter 11 elaborates two model field theories, statistical neural field theory and spin glass theory. Chapter 12 develops the smart network field theory in detail with system elements, operation, and criticality detection measures, and considers applications in blockchain and deep learning smart network systems.
Part 5 extends the smart network field theory into the quantum realm with a model called the AdS/CFT correspondence (also known as gauge/gravity duality and the bulk–boundary correspondence) and holographic codes. Chapter 13 describes the holographic principle and its formalization in the AdS/CFT correspondence, and work connecting physical theory to information theory through the correspondence. Chapter 14 discusses the quantitative mobilization of the AdS/CFT correspondence into holographic quantum error-correcting codes.
Part 6 posits quantum smart networks as the quantum instantiation of smart networks and elaborates the smart network quantum field theory. In Chapter 15, a number of speculative conjectures are presented as to how the smart network quantum field theory may be engaged in a holographic format based on the bulk–boundary correspondence. The risks, limitations, and farther consequences of the work are discussed, proposing the possibility of multiple future eras of quantum computing.
References
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Part 1
Smart Networks and Quantum Computing
Chapter 2
Smart Networks: Classical and Quantum Field Theory
Abstract
This work aims to establish a deeper connection between physics and information technology. Smart network theory is proposed as a physical theory of network technologies, particularly to encompass a potential expansion into the quantum computing domain. The objective is to elaborate a physical basis for technology theories that is easily deployable in the design, operation, and catalytic emergence of next-generation smart network systems. The general notion of smart network theory as a physical basis for smart network technologies is developed into a smart network field theory (SNFT) and a smart network quantum field theory (SNQFT) relevant to the two scale domains. The intuition is that the way to orchestrate many-particle systems from a characterization, control, criticality, and novelty emergence standpoint is through an SNFT and an SNQFT. Such theories should be able to make relevant predictions about smart network systems.
2.1Smart Networks
Smart networks are intelligent autonomous networks, an emerging form of global computational infrastructure, in which decision-making and self-operating capabilities are built directly into the software. Examples of smart networks include blockchain economic networks, deep learning pattern recognition networks, unmanned aerial vehicles, real-time bidding (RTB) for advertising, and high-frequency trading (HFT) networks. More formally, smart networks are state machines that make probabilistic guesses about reality states of the world and act automatically based on these guesses. Communications networks are becoming computational networks in the sense of running executable code. Smart networks are a contemporary feature of reality with possibly thousands to billions of constituent elements, and thus entail a theoretically-robust model for their design and operation. Using statistical physics (statistical neural field theory and spin-glass models) and information theory (the anti-de Sitter space/conformal field theory, AdS/CFT, correspondence), this work proposes SNFTs for the orchestration of the fleet-many items in smart network systems.
2.2Smart Network Theory
2.2.1 Conventional (SNFT) and (SNQFT)
There is an urgent motivation for the development of smart network theories. Smart network technologies are being demonstrated as domains of complexity (exhibiting the behavior of complex systems), which are challenging to understand and manage. Smart networks are like quantum many-body systems in which interactions become too complex to model directly. Many smart network technologies are effectively a black box whose operations are either unknown from the outset (deep learning networks), or becoming hidden through zero-knowledge proofs (block-chain economic networks). Simultaneously, smart network technologies are experiencing rapid worldwide adoption, becoming unwieldy in scale, and possibly migrating to quantum computers.
The research intuition is that a technophysics approach (the application of physics principles to the technology domain) is warranted for the further development of smart network technologies. The smart network theories proposed in this work, the smart network field theory (SNFT) and the smart network quantum field theory (SNQFT), are designed to provide an integrated theoretical basis for smart network technologies that are rooted in physical foundations. The smart network theories can be used to orchestrate many-particle systems from a characterization, control, criticality, and novelty emergence perspective.
2.2.2 Smart network technologies are quantum-ready
Smart network technologies are already instantiated in 3D formats (computation graphs which imply programmability and analytic solvability), which makes them somewhat quantum-ready for the next steps towards deployment in quantum computers. The 3D format suggests the possibility of instantiation in quantum information systems with superposition and multi-dimensional spaces. One next step for implementation in quantum computers would be writing smart network technologies in the form of quantum-computable properties, namely superposition, entanglement, interference (SEI properties are the quantum computable properties).
Smart network technologies are quantum-ready, meaning 3D, as a by-product of their being instantiated in a computational
