Physical information

Physical information is a form of information. In physics, it refers to the information of a physical system. Physical information is an important concept used in a number of fields of study in physics. For example, in quantum mechanics, the form of physical information known as quantum information is used to describe quantum phenomena such as entanglement and superposition.[1][2][3][4][5][6] In thermodynamics and statistical mechanics, the concept of physical information is likewise used to describe phenomena related to thermodynamic entropy. (See Entropy in thermodynamics and information theory for an overview of this subject). The concept of information is also important in relativity, since correlations between events in spacetime can be measured in terms of physical information.[7][8][9][10][11][12]

In a general sense, information is that which resolves uncertainty about the state of a physical system at a given moment in time. Information can also be understood as a measure of probability as follows: a physical state with a low initial probability of observation contains a relatively high quantity of physical information, while a state with a high initial probability of observation contains a relatively low quantity of physical information.

When clarifying the subject of information, care should be taken to distinguish between the following specific cases:

  • The phrase instance of information refers to the specific instantiation of information (identity, form, essence) that is associated with the being of a particular example of a thing. (This allows for the reference to separate instances of information that happen to share identical patterns.)
  • A holder of information is a variable or mutable instance that can have different forms at different times (or in different situations).
  • A piece of information is a particular fact about a thing's identity or properties, i.e., a portion of its instance.
  • A pattern of information (or form) is the pattern or content of an instance or piece of information. Many separate pieces of information may share the same form. We can say that those pieces are perfectly correlated or say that they are copies of each other, as in copies of a book.
  • An embodiment of information is the thing whose essence is a given instance of information.
  • A representation of information is an encoding of some pattern of information within some other pattern or instance.
  • An interpretation of information is a decoding of a pattern of information as being a representation of another specific pattern or fact.
  • A subject of information is the thing that is identified or described by a given instance or piece of information. (Most generally, a thing that is a subject of information could be either abstract or concrete; either mathematical or physical.)
  • An amount of information is a quantification of how large a given instance, piece, or pattern of information is, or how much of a given system's information content (its instance) has a given attribute, such as being known or unknown. Amounts of information are most naturally characterized in logarithmic units.

As the above usages are all conceptually distinct from each other, overloading the word "information" (by itself) to denote (or connote) several of these concepts simultaneously can lead to confusion. Accordingly, this article uses more detailed phrases, such as those shown in bold above, whenever the intended meaning is not made clear by the context.

Classical versus quantum information

The instance of information that is contained in a physical system is generally considered to specify that system's "true" state. (A realist would assert that a physical system always has a true state of some sort—whether classical or quantum—even though, in many practical situations, the system's true state may be largely unknown.)

When discussing the information that is contained in physical systems according to modern quantum physics, we must distinguish between classical information and quantum information. Quantum information specifies the complete quantum state vector (or equivalently, wavefunction) of a system, whereas classical information, roughly speaking, only picks out a definite (pure) quantum state if we are already given a prespecified set of distinguishable (orthogonal) quantum states to choose from; such a set forms a basis for the vector space of all the possible pure quantum states (see pure state). Quantum information could thus be expressed by providing (1) a choice of a basis such that the actual quantum state is equal to one of the basis vectors, together with (2) the classical information specifying which of these basis vectors is the actual one. (However, the quantum information by itself does not include a specification of the basis, indeed, an uncountable number of different bases will include any given state vector.)

Note that the amount of classical information in a quantum system gives the maximum amount of information that can actually be measured and extracted from that quantum system for use by external classical (decoherent) systems, since only basis states are operationally distinguishable from each other. The impossibility of differentiating between non-orthogonal states is a fundamental principle of quantum mechanics, equivalent to Heisenberg's uncertainty principle. Because of its more general utility, the remainder of this article will deal primarily with classical information, although quantum information theory does also have some potential applications (quantum computing, quantum cryptography, quantum teleportation) that are currently being actively explored by both theorists and experimentalists.[13]

Quantifying classical physical information

An amount of (classical) physical information may be quantified, as in information theory, as follows.[14] For a system S, defined abstractly in such a way that it has N distinguishable states (orthogonal quantum states) that are consistent with its description, the amount of information I(S) contained in the system's state can be said to be log(N). The logarithm is selected for this definition since it has the advantage that this measure of information content is additive when concatenating independent, unrelated subsystems; e.g., if subsystem A has N distinguishable states (I(A) = log(N) information content) and an independent subsystem B has M distinguishable states (I(B) = log(M) information content), then the concatenated system has NM distinguishable states and an information content I(AB) = log(NM) = log(N) + log(M) = I(A) + I(B). We expect information to be additive from our everyday associations with the meaning of the word, e.g., that two pages of a book can contain twice as much information as one page.

The base of the logarithm used in this definition is arbitrary, since it affects the result by only a multiplicative constant, which determines the unit of information that is implied. If the log is taken base 2, the unit of information is the binary digit or bit (so named by John Tukey); if we use a natural logarithm instead, we might call the resulting unit the "nat." In magnitude, a nat is apparently identical to Boltzmann's constant k or the ideal gas constant R, although these particular quantities are usually reserved to measure physical information that happens to be entropy, and that are expressed in physical units such as joules per kelvin, or kilocalories per mole-kelvin.

Physical information and entropy

An easy way to understand the underlying unity between physical (as in thermodynamic) entropy and information-theoretic entropy is as follows:

Entropy is simply that portion of the (classical) physical information contained in a system of interest (whether it is an entire physical system, or just a subsystem delineated by a set of possible messages) whose identity (as opposed to amount) is unknown (from the point of view of a particular knower).

This informal characterization corresponds to both von Neumann's formal definition of the entropy of a mixed quantum state (which is just a statistical mixture of pure states; see von Neumann entropy), as well as Claude Shannon's definition of the entropy of a probability distribution over classical signal states or messages (see information entropy).[14] Incidentally, the credit for Shannon's entropy formula (though not for its use in an information theory context) really belongs to Boltzmann, who derived it much earlier for use in his H-theorem of statistical mechanics.[15] (Shannon himself references Boltzmann in his monograph.[14])

Furthermore, even when the state of a system is known, we can say that the information in the system is still effectively entropy if that information is effectively incompressible, that is, if there are no known or feasibly determinable correlations or redundancies between different pieces of information within the system. Note that this definition of entropy can even be viewed as equivalent to the previous one (unknown information) if we take a meta-perspective, and say that for observer A to "know" the state of system B means simply that there is a definite correlation between the state of observer A and the state of system B; this correlation could thus be used by a meta-observer (that is, whoever is discussing the overall situation regarding A's state of knowledge about B) to compress his own description of the joint system AB.[16]

Due to this connection with algorithmic information theory,[17] entropy can be said to be that portion of a system's information capacity which is "used up," that is, unavailable for storing new information (even if the existing information content were to be compressed). The rest of a system's information capacity (aside from its entropy) might be called extropy, and it represents the part of the system's information capacity which is potentially still available for storing newly derived information. The fact that physical entropy is basically "used-up storage capacity" is a direct concern in the engineering of computing systems; e.g., a computer must first remove the entropy from a given physical subsystem (eventually expelling it to the environment, and emitting heat) in order for that subsystem to be used to store some newly computed information.[16]

Extreme physical information
Main article: Extreme physical information

In a theory developed by B. Roy Frieden,[18][19][20][21] "physical information" is defined as the loss of Fisher information that is incurred during the observation of a physical effect. Thus, if the effect has an intrinsic information level J but is observed at information level I, the physical information is defined to be the difference I − J. Because I and J are functionals, this difference defines an informational Lagrangian. Frieden's principle of extreme physical information (EPI), which is analogous to the principle of stationary action, states that minimizing the quantity I − J yields equations that correctly describe the evolution of a given physical system over time. However, the EPI principle has been met with considerable criticism within the scientific community.[22] The EPI principle should not be confused with the more conventional principle of maximum entropy used in maximum entropy thermodynamics.

See also

References
  1. Vedral, Vlatko. (2018). Decoding Reality : The Universe as Quantum Information. Oxford University Press. ISBN 978-0-19-881543-3. OCLC 1038430295.
  2. Entangled world : the fascination of quantum information and computation. Audretsch, Jürgen, 1942-. Weinheim: Wiley-VCH. 2006. ISBN 978-3-527-61909-2. OCLC 212178399.CS1 maint: others (link)
  3. Schumacher, Benjamin. (2010). Quantum processes, systems, and information. Westmoreland, Michael D. New York: Cambridge University Press. ISBN 978-0-511-67753-3. OCLC 663882708.
  4. Khrennikov, Andrei (July 2016). "Reflections on Zeilinger-Brukner information interpretation of quantum mechanics". Foundations of Physics. 46 (7): 836–844. arXiv:1512.07976. Bibcode:2016FoPh...46..836K. doi:10.1007/s10701-016-0005-z. ISSN 0015-9018. S2CID 119267791.
  5. Lloyd, Seth, 1960- (2006). Programming the universe : a quantum computer scientist takes on the cosmos (1st ed.). New York: Knopf. ISBN 1-4000-4092-2. OCLC 60515043.CS1 maint: multiple names: authors list (link)
  6. Susskind, Leonard (25 February 2014). Quantum mechanics : the theoretical minimum. Friedman, Art. New York. ISBN 978-0-465-03667-7. OCLC 853310551.
  7. Glattfelder, James B. (2019), Glattfelder, James B. (ed.), "A Universe Built of Information", Information—Consciousness—Reality: How a New Understanding of the Universe Can Help Answer Age-Old Questions of Existence, The Frontiers Collection, Cham: Springer International Publishing, pp. 473–514, doi:10.1007/978-3-030-03633-1_13, ISBN 978-3-030-03633-1, retrieved 2020-11-01
  8. Peres, Asher; Terno, Daniel R. (2004-01-06). "Quantum information and relativity theory". Reviews of Modern Physics. 76 (1): 93–123. arXiv:quant-ph/0212023. Bibcode:2004RvMP...76...93P. doi:10.1103/RevModPhys.76.93. S2CID 7481797.
  9. Wheeler, John Archibald (1989), "Information, Physics, Quantum: The Search for Links", Proceedings III International Symposium on Foundations of Quantum Mechanics, pp. 354–358, retrieved 2020-11-01
  10. Moskowitz, Clara. "Tangled Up in Spacetime". Scientific American. Retrieved 2020-11-01.
  11. Cowen, Ron (2015-11-19). "The quantum source of space-time". Nature News. 527 (7578): 290–293. Bibcode:2015Natur.527..290C. doi:10.1038/527290a. PMID 26581274. S2CID 4447880.
  12. "ShieldSquare Captcha". iopscience.iop.org. Retrieved 2020-11-01.
  13. Michael A. Nielsen and Isaac L. Chuang, Quantum Computation and Quantum Information, Cambridge University Press, 2000.
  14. Claude E. Shannon and Warren Weaver, Mathematical Theory of Communication, University of Illinois Press, 1963.
  15. Carlo Cercignani, Ludwig Boltzmann: The Man Who Trusted Atoms, Oxford University Press, 1998.
  16. Michael P. Frank, "Physical Limits of Computing", Computing in Science and Engineering, 4(3):16-25, May/June 2002. http://www.cise.ufl.edu/research/revcomp/physlim/plpaper.html
  17. W. H. Zurek, "Algorithmic randomness, physical entropy, measurements, and the demon of choice," in (Hey 1999), pp. 393-410, and reprinted in (Leff & Rex 2003), pp. 264-281.
  18. Frieden, B. Roy; Gatenby, Robert A. (2005-09-01). "Power laws of complex systems from extreme physical information". Physical Review E. 72 (3): 036101. arXiv:q-bio/0507011. Bibcode:2005PhRvE..72c6101F. doi:10.1103/physreve.72.036101. ISSN 1539-3755. PMID 16241509. S2CID 17987848.
  19. Frieden, B. Roy; Soffer, Bernard H. (2006-11-16). "Information-theoretic significance of the Wigner distribution". Physical Review A. 74 (5): 052108. arXiv:quant-ph/0609157. Bibcode:2006PhRvA..74e2108F. doi:10.1103/physreva.74.052108. ISSN 1050-2947. S2CID 55541671.
  20. Frieden, B. Roy; Soffer, Bernard H. (1995-09-01). "Lagrangians of physics and the game of Fisher-information transfer". Physical Review E. 52 (3): 2274–2286. Bibcode:1995PhRvE..52.2274F. doi:10.1103/physreve.52.2274. ISSN 1063-651X. PMID 9963668.
  21. B. Roy Frieden, Science from Fisher Information, Cambridge University Press, 2004.
  22. Lavis, D. A.; Streater, R. F. (2002-06-01). "Physics from Fisher information". Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics. 33 (2): 327–343. doi:10.1016/S1355-2198(02)00007-2. ISSN 1355-2198.

Further reading
  • J. G. Hey, ed., Feynman and Computation: Exploring the Limits of Computers, Perseus, 1999.
  • Harvey S. Leff and Andrew F. Rex, Maxwell's Demon 2: Entropy, Classical and Quantum Information, Computing, Institute of Physics Publishing, 2003.
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