Reversible computing: Difference between revisions

Revision as of 18:39, 15 June 2023

Revision as of 21:38, 23 June 2023

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A process is said to be ''physically reversible'' if it results in no increase in physical [[entropy]]; it is [[isentropic]]. There is a style of circuit design ideally exhibiting this property that is referred to as '''charge recovery logic'''<!--boldface per WP:R#PLA-->, [[adiabatic circuit]]s, or '''adiabatic computing'''<!--boldface per WP:R#PLA--> (see [[Adiabatic process]]). Although ''in practice'' no nonstationary physical process can be ''exactly'' physically reversible or isentropic, there is no known limit to the closeness with which we can approach perfect reversibility, in systems that are sufficiently well isolated from interactions with unknown external environments, when the laws of physics describing the system's evolution are precisely known.

A motivation for the study of technologies aimed at implementing reversible computing is that they offer what is predicted to be the only potential way to improve the [[computational energy efficiency]] (i.e., useful operations performed per unit energy dissipated) of computers beyond the fundamental [[von Neumann-Landauer limit|von Neumann–Landauer limit]]<ref name="landauer">{{Citation |author=Rolf Landauer |url=http://worrydream.com/refs/Landauer%20-%20Irreversibility%20and%20Heat%20Generation%20in%20the%20Computing%20Process.pdf |title=Irreversibility and heat generation in the computing process |journal=IBM Journal of Research and Development |volume=5 |issue=3 |pages=183–191 |year=1961 |accessdate=2015-02-18 |doi=10.1147/rd.53.0183 |quote=The entropy of a closed system, e.g., a computer with its own batteries, cannot decrease; hence this entropy must appear elsewhere as a heating effect, supplying 0.6931 kT per restored bit to the surroundings.}}</ref><ref name="neumann">{{cite book|author=J. von Neumann|author-link=John von Neumann|publisher=University of Illinois Press|title=Theory of self-reproducing automata|year=1966|url=https://archive.org/details/theoryofselfrepr00vonn_0|access-date=2022-05-21}} Third lecture: Statistical Theories about Information</ref> of {{Math|''[[kT (energy)|kT]]'' ln(2)}} energy dissipated per irreversible [[bit operation]]. Although the Landauer limit was millions of times below the energy consumption of computers in the 2000s and thousands of times less in the 2010s,<ref>{{cite journal |last1=Bérut |first1=Antoine |last2=Arakelyan |first2=Artak |last3=Petrosyan |first3=Artyom |last4=Ciliberto |first4=Sergio |last5=Dillenschneider |first5=Raoul |last6=Lutz |first6=Eric |title=Experimental verification of Landauer's principle linking information and thermodynamics |journal=Nature |date=March 2012 |volume=483 |issue=7388 |pages=187–189 |doi=10.1038/nature10872 |pmid=22398556 |bibcode=2012Natur.483..187B |arxiv=1503.06537 |s2cid=9415026 }}</ref> proponents of reversible computing argue that this can be attributed largely to architectural overheads which effectively magnify the impact of Landauer's limit in practical circuit designs, so that it may prove difficult for practical technology to progress very far beyond current levels of energy efficiency if reversible computing principles are not used.<ref>Michael P. Frank, "Foundations of Generalized Reversible Computing," to be published at the 9th Conference on Reversible Computation, Jul. 6-7, 2017, Kolkata, India. Preprint available at https://cfwebprod.sandia.gov/cfdocs/CompResearch/docs/grc-rc17-preprint2.pdf.</ref>

==Relation to thermodynamics==

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{{Unreferenced section|date=July 2022}}

Landauer's principle (and indeed, the [[second law of thermodynamics]] itself) can also be understood to be a direct [[logical consequence]] of the underlying [[CPT symmetry|reversibility of physics]], as is reflected in the [[Hamiltonian mechanics|general Hamiltonian formulation of mechanics]], and in the [[time evolution|unitary time-evolution operator]] of [[quantum mechanics]] more specifically.<ref>{{Cite journal |last=Frank |first=Michael P. |last2=Shukla |first2=Karpur |date=June 1, 2021 |title=Quantum Foundations of Classical Reversible Computing |url=https://www.mdpi.com/1099-4300/23/6/701 |journal=Entropy |language=en |volume=23 |issue=6 |pages=701 |doi=10.3390/e23060701 |issn=1099-4300 |pmc=PMC8228632 |pmid=34206044}}</ref>

The implementation of reversible computing thus amounts to learning how to characterize and control the physical dynamics of mechanisms to carry out desired computational operations so precisely that we can accumulate a negligible total amount of uncertainty regarding the complete physical state of the mechanism, per each logic operation that is performed. In other words, we would need to precisely track the state of the active energy that is involved in carrying out computational operations within the machine, and design the machine in such a way that the majority of this energy is recovered in an organized form that can be reused for subsequent operations, rather than being permitted to dissipate into the form of heat.