The paper describes the general physical theory of signals, carriers of information, which supplements Shannon’s abstract classical theory and is applicable in much broader fields, including nuclear physics. It is shown that in the absence of classical noise its place should be taken by the physical threshold of signal perception for objects of both macrocosm and microcosm. The signal perception threshold allows the presence of subthreshold (virtual) signal states. For these states, Boolean algebra of logic ( A = 0/1) is transformed into the “algebraic logic” of probabilities (0 ≤ a ≤ 1). The similarity and difference of virtual states of macroand microsignals are elucidated. “Real” and “quantum” information for computers is considered briefly. The maximum information transmission rate is estimated based on physical constants.
The paper describes the general physical theory of signals, carriers of information, which supplements Shannon’s abstract classical theory and is applicable in much broader fields, including nuclear physics. It is shown that in the absence of classical noise its place should be taken by the physical threshold of signal perception for objects of both macrocosm and microcosm. The signal perception threshold allows the presence of subthreshold (virtual) signal states. For these states, Boolean algebra of logic ( A = 0/1) is transformed into the “algebraic logic” of probabilities (0 ≤ a ≤ 1). The similarity and difference of virtual states of macroand microsignals are elucidated. “Real” and “quantum” information for computers is considered briefly. The maximum information transmission rate is estimated based on physical constants.
It is known that the transition from quantum to classical physics occurs when the action constant h decreases to an infinitesimal value. As shown in the article, for signals this statement is applicable to their classical model only and it is far from all real signals existing in nature and having a threshold on action more than h. Such threshold is the threshold of perception that exists for any natural macro and micro signal receivers, including atoms and particles. It was revealed that above the boundary frequency of the classical thermal noise v-high, found by Planck, the properties of real signals correspond to quantum constraints. It is asserted that in the frequency range above v-high inertness appears and dominates, i.e. the integration of the signals. As a result of it a transition from the perception threshold on energy to the perception threshold on action happens. Besides, instead of disappearing thermal noise on energy, an indestructible sub-threshold noise on action appears that is associated with the uncertainty relation. The concept of algebraic, not integer logic is introduced for real sub-threshold signals. It is demonstrated that in combination of sub-threshold random states of several signals, their integration leads to a joint probability, i.e. to superposition states of QM.
A classical signal which agrees with the ideas of mathematical analysis on infinitesimal quantities possesses an infinite information capacity, which contradicts the concept of information that is finite by definition. A real signal should have some “threshold” for determining a finite step between its distinguishable states and the “limit” for the possible number of states. This paper considers the influence of the level of required energy, performance, and noise of the receiver on the capability of distinguishing the set of states in the signal. It is shown that the generalized threshold is the constraint on the spectral density of the signal energy and the limit is the constraint on the energy density with respect to time or spatial coordinate. In the microcosm, a signal is a gradient of potential; the constraint of its perception speed in the case of motion results, within the action threshold, in the fact that it changes in kinetic energy with changing potential cannot be determined. As a result, the particle motion turns out defined only in a finite number of “reference” points separated by intervals determined by the information threshold. Between reference points the motion has an uncertain, random, i.e., noisy character. The threshold equation for finding random deviations of motion parameters which should generalize the known Schrödinger and Klein-Gordon equations is discussed. Information transmission capacity of space for electromagnetic signals is estimated based on the analysis of the densities of the threshold and limiting energy.
What kind of object is a quantum state? Is it an object that encodes an exponentially growing amount of information (in the size of the system) or more akin to a probability distribution? It turns out that these questions are sensitive to what we do with the information. For example, Holevo's bound tells us that n qubits only encode n bits of classical information but for certain communication complexity tasks there is an exponential separation between quantum and classical resources. Instead of just contrasting quantum and classical physics, we can place both within a broad landscape of physical theories and ask how non-quantum (and non-classical) theories are different from, or more powerful than quantum theory. For example, in communication complexity, certain (non-quantum) theories can trivialise all communication complexity tasks. In recent work [C. M. Lee and M. J. Hoban, Proc. Royal Soc. A 472 (2190), 2016], we showed that the immense power of the information content of states in general (non-quantum) physical theories is not limited to communication complexity. We showed that, in general physical theories, states can be taken as "advice" for computers in these theories and this advice allows the computers to easily solve any decision problem. Aaronson has highlighted the close connection between quantum communication complexity and quantum computations that take quantum advice, and our work gives further indications that this is a very general connection. In this work, we review the results in our previous work and discuss the intricate relationship between communication complexity and computers taking advice for general theories.
What is information? A short answer could be: Anything that changes our minds and states of thinking! Information is virtual. It can be carried by speech, an image, scratches on a stone, patterns in a photon field, the connections of neurons in our brains or even by the wavefunction of an electron. Information does not depend on the actual means of transmission nor does its identity change when changing the carrier. Information is physical. It needs to be sustained by a physical substrate and rearranging that substrate to contain the wanted information requires to spend energy or work. Without a physical world, information can not be stored, processed, or transmitted. Our world is permeated with different representations of information. Actually Information about the physical laws governing our Universe can be found everywhere and in everything. Could it then be that the real fundamental elements of this world are tiny bits of information? Could we get it from bit?1 A number of researcher investigate this possibility, and several of their attempts to recover physical laws from the principles governing information are reported about in this special issue on physics of information. The articles by Gregg Jaeger,2 by Ariel Caticha,3 and by John Skilling and Kevin Knuth4 address the reconstruction of quantum mechanics from information theory. Furthermore, Kevin Knuth and James Walsh5 show how to recover space-time from particles sending each others signals in an initially geometry-free setting. A central element of most works on information is the concept of a probability. Probabilities can be defined via various routes. Most familiar might be their definitions by the frequentists school as relative frequencies of events (in the limit of infinite amounts of such). A more general definition of probabilities is the Bayesians' one, which regards probabilities as quantifiers of statements, expressing how much those should be expected to be true. Although this definition looks subjective on the first glance, it rests on a solid mathematical theorem by Richard Cox6 that any extension of binary logic to a continuum of single valued truthfulness quantifier that respects a minimum of requirements must be isomorphic to probability theory. And this definition turns out to embrace the frequentists' definition in case that infinite numbers of comparable events are actually available and can be counted. The majority of authors of this special issues seem to favor the Bayesian perspective, which is probably a result of our own, the guest editors', preference and not necessarily a reflection of the prevailing position of contemporary scientists. The different views on probabilities of frequentists and Bayesians are addressed by the article of Allen Caldwell.7 He builds a bridge between these antagonistic camps by explaining frequentists' constructions in a Bayesian language. In particular, his argumentation why the omnipresent p-value in statistical and experimental literature might often capture a relevant signal of model misfit is reassuring, given that a large fraction of our medical system is based on decisions backed up by p-values. A Bayesian might add that it would be even more reassuring, if a system rigorously build on logic and its continuous extension would be used for decisions affecting live-or-death questions. The laws governing probabilities themselves seem to reflect that of more elementary mathematical structures. Kevin Knuth shows how these emerge from lattice theory, which deals with relations in partially ordered sets like that of collections of possibilities.8 The sum rule of probability theory is a direct consequence of the underlying lattice structure. Interestingly, a number of other theories are resting also on such lattices and therefore exhibit their own versions of sum rules: measures of sets, mutual information, the Euler characteristics of topological spaces, as well as quantum mechanical interactions that are quantified by pairs of numbers, the real and imaginary parts of complex probability amplitudes (see also4). The theory of information has very practical application in physics and elsewhere. Manfred Opper discusses how the solutions of stochastic differential equations can be inferred via variational methods, which have their origin in thermodynamics.9 Torsten Enßlin introduces information field theory (IFT), the information theory for fields, as a means to infer the configuration of a physical field from incomplete and noisy data.10 The mathematical apparatus for this is that of quantum field theory (QFT), just with ordinary probabilities instead of complex probability amplitudes as in the latter. Could it be that the difference between IFT and QFT is just be the same as between classical information theory and quantum mechanics, the transition from real valued to complex valued quantifier for statements? Anyhow, any practical inference machinery, be it for dynamical systems or for fields, requires sophisticated numerical methods. Michael Betancourt reviews Markov Chain Monte Carlo Methods to calculate expectation values over high dimensional probabilities.11 Theo Steininger and coworkers present the NIFTy3 software package for numerical information field theory calculations, which can handle very high dimensional field inference problems.12 Finally, in an article focused on the subtleties of the Monty Hall problem and mostly aiming at information theoretical entertainment, Torsten Enßlin and Margret Westercamp investigate the possibility that human intuition might be more rational than contemporary psychology thinks.13 A number of possible topics for an issue on physics of information are not addressed here. We would have loved to have articles on Landauer's principle, which states that erasing information has to cost energy, on QBism, a Bayesian version of quantum mechanics, on artificial and biological neural networks, as well as any other aspect of the information-soaked biological world. What has a reader of this special issue to be equipped with in order to follow the lines of thoughts? In addition to the usual physicist's education, the minimal additional equipment are the basics of probability theory and the concept of entropy. Most of the other concepts used in the articles rest on those and are introduced there. Let A, B, and C be propositions (or possibilities). The probability P ( A | B ) of A being true assuming that B is true is a number between 0 and 1, with P ( A | B ) = 1 expressing certainty of A under B ( B ⇒ A ) and P ( A | B ) = 0 expressing certainty of not A being the case under B ( B ⇒ ¬ A ).
In these first few chapters, our aim is to establish a firm grounding so that we can address some fundamental questions regarding information transmission over quantum channels. This area of study has become known as “quantum Shannon theory” in the broader quantum information community, in order to distinguish this topic from other areas of study in quantum information science. In this text, we will use the terms “quantum Shannon theory” and “quantum information theory” somewhat interchangeably. We will begin by briefly overviewing several fundamental aspects of the quantum theory. Our study of the quantum theory, in this chapter and future ones, will be at an abstract level, without giving preference to any particular physical system such as a spin-1/2 particle or a photon. This approach will be more beneficial for the purposes of our study, but, here and there, we will make some reference to actual physical systems to ground us in reality. You may be wondering, what is quantum Shannon theory and why do we name this area of study as such? In short, quantum Shannon theory is the study of the ultimate capability of noisy physical systems, governed by the laws of quantum mechanics, to preserve information and correlations. Quantum information theorists have chosen the name quantum Shannon theory to honor Claude Shannon, who single-handedly founded the field of classical information theory, with a groundbreaking 1948 paper (Shannon, 1948).
The Newton’s laws had been developed on basis experiments with nature signals, and by hand computation for processing of these signals. The signal carries the code of data. The carrier is a wave - the process of transfer in space and time (physical space) of the code. Then the parameters of this transfer determines the state of the signals in time and space. The design of quantum communication systems, information technology, etc. modeling of quantum signals is a complex problem of representation of the carrier, the signal code, and their quantum states. Quantum computing, and explaining on resolving of application tasks (RSA, FFT algorithm, quantum circuits, etc.) in physical the scales theories and practices (in mechanics and quantum mechanics) are different in technics, algorithmic, and the simulation. All of these significantly does complexity of the design of quantum communication systems. This paper discusses the representation of a quantum signal as its general relationship with the difference of digitized sinusoidal functions with different digitization parameters obtained by a classical computer. This difference is considered similar to squeezed states with different sets of digitization parameters. The peculiarities of the obtained difference signatures with respect to similar quantum phenomena are classified. The representation of modeling of a quantum signal by possibilities of the classical computer as it was developed for quantum states, schemes, protocols is offered.