may become available in the next few decades. The path is clear but non-trivial, the task is scaling up the number of qubits from 30–70 to hundreds, thousands, and millions, and testing various quantum error correction schemes for the end goal of constructing a universal fault-tolerant quantum computer.
Many kinds of quantum computing hardware are being demonstrated, and it is possible that optical quantum computing could accelerate in the same vein as optical global communications did previously, this time for the quantum internet with satellite-based quantum key distribution, secure end-to-end communications, quantum routers and modems, and distributed quantum computing. Smart network technologies such as blockchain and deep learning are sponsoring the advance to quantum computing with post-quantum cryptography and quantum machine learning optimization and simulation, and other smart network technologies could follow suit, such as autonomous vehicle networks, robotic swarms, automated supply chains, and industrial robotics cloudminds. Quantum computing is not just nice, but necessary to keep pace with growing data volumes, and to enable a new slate of applications such as whole brain emulation, atomically-precise manufacturing, and causal models for disease.
1.2Technophysics
Technophysics is the application of physics principles to the study of technology, by analogy to Biophysics and Econophysics. Biophysics is an interdisciplinary science that uses the approaches and methods of physics to study biological systems with the goal of understanding the structure, dynamics, interactions, and functions of biological systems. Likewise, Econophysics is an interdisciplinary field of research that applies theories and methods developed in physics to solve problems in economics and finance, particularly those including uncertainty, stochastic processes, and nonlinear dynamics. Considering Technophysics, although much of technology development is initially physics-based, a wider range of concepts and methods from ongoing advances in physics could be applied to the study and development of new technology. In this work, the techno-physics approach is directed at the study of smart network technologies (intelligent self-operating networks such as blockchains and deep learning neural networks), by engaging statistical physics and information theory for the characterization, control, criticality assessment, and novelty catalysis of smart network systems.
Technophysics arises from a confluence of factors in contemporary science. First, there have been several non-trivial connections in the last few decades linking the physical world and the representational world of mathematics, algorithms, and information theory. One example is that statistical physics has made a link between spin glasses in condensed matter physics and combinatorial optimization problems. Specifically, an association between the glass transition in condensed matter physics, error-correcting codes in information theory, and probability theory in computer science has been established with statistical physics (Mezard & Montanari, 2009). Another example is the symbiotic relationship of using machine learning algorithms to understand physical systems, and using analogies with physical systems and materials to understand the operation of algorithms (Krzakala et al., 2007). A third example is the holographic principle (Susskind, 1995), formalized in the AdS/CFT correspondence (Maldacena, 1998), and its link to information theory (Harlow & Hayden, 2013), quantum error correction codes (Pastawski et al., 2015), and tensor networks (Witten, 2016). The holographic principle suggests that in any physical system, there is a correspondence between a volume of space and its boundary region. The implication is that the interior bulk can be described by a boundary theory in one fewer dimensions, in an information compression mechanism between the 3D bulk and the 2D boundary.
A second factor giving rise to technophysics is a cross-pollination in academic methods. Various approaches have become part of the cannon of scientific methods irrespective of field. These methods include complexity science, network theory, information science, and computation graphs. Many fields now have a computational information science counterpart, both in the hard sciences (such as computational neuroscience, computational chemistry, and computational astrophysics), and in liberal arts (seen in the digital humanities, semantic data structure investigation, and text mining and analysis). The opposite trend is also true, developing a physics-based counterpart to fields of study, such as in Biophysics, Econophysics, and Technophysics. The exchange between academic methods is leading to the development of a more comprehensive and robust apparatus of universal scientific study across all areas of the academe.
1.2.1 Conceptual toolkit of ideas
The premise of this text is that it is necessary to be facile with technophysics ideas to be effective in today’s world. Hence, a compendium of knowledge modules to enable such facility is presented in the course of this book. Having a grasp of concepts and methods from a broader range of fields that extend beyond an initial field of training, especially from contemporary research frontiers and interstices, could increase the capacity for innovation and impact in the world. This book provides a strategy for managing the considerable uncertainty of the disruptive possibilities of quantum computing in the next decades and beyond with smart network field theories as a tool. Methods are integrated from statistical and theoretical physics, information theory, and computer science. The techno-physics approach is rooted in theory and application, and presented at the levels of both defining a conceptual understanding as well as outlining how formalisms and analysis techniques may be used in practice.
It is possible to elaborate some of the standardized ideas that comprise the canon of technophysics knowledge. These include eigenvalues and eigenvectors, and the notion of operators that measure the multidimensional configuration of a system and that can act on the system. The Hamiltonian is the operator par excellence that measures the total energy in a system, and allows problems to be written in the form of an energy minimization problem. Complementarity (only one property of a quantum system can be measured at a time), time dilation (the system looks different to different viewers), and geometry-based metrics are important. There is a notion of selectable parameters, among different geometries, coordinate systems, and space and time domains.
In some sense, the terms network, graph, tensor, matrix, operator, and 3D all point at the same kind of concept, and while not technically synonyms, mutually imply each other as properties of a computable system. A computation graph is a network, a network implies a computation graph or matrix, and all are 3D or of higher-order dimensionality. Further, an operator is a matrix, a field is a matrix, and tensor networks and random tensor networks can be used to rewrite high-dimensional entangled problem domains to be analytically computable. Dimensionality portability, rescaling, and reduction are frequently-used techniques. Answers are likely to be given in probabilistic terms. The bulk–boundary correspondence is a conceptual structure in which a system can be written as two regions, a surface in one fewer dimensions that can be used to interrogate the bulk region as a more complex and possibly unknowable domain.
A key technophysics principle is reducing or rendering a physical system into a form that is computable and then analytically solving it. Computational complexity is a constant focus. Classical problems can be made quantum-computable by writing them with SEI properties (superposition, entanglement, and interference), meaning converting the problem to a structure that engages the quantum properties of superposition, entanglement, and interference. As a gross heuristic, quantum computers may allow a one-tier increase in the computational complexity schema of problem calculation. Any problem that takes exponential time in classical systems (i.e. too long) may take polynomial time in quantum systems (i.e. a reasonable amount of time for practical use). In the canonical Traveling Salesman Problem, perhaps twice as many cities could be checked in half the time using a quantum computer.
1.2.2 New slate of all-purpose smart technology features
Smart network technologies are creating a variety of new technology developments as standard features to solve important problems, and that have greater extensibility beyond their initial purpose. One example is consensus algorithms, which provide cryptographic security of block-chains through the mining operation, and more generally comprise a standard technology (ConsensusTech) that could be used
