Unconventional computing: Difference between revisions

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{{main|Model of computation}}

A model of computation describes how the output of a mathematical function is computed given its input. The model describes how units of computations, memories, and communications are organized.<ref>{{cite book|last=Savage|first=John E.|author-link = John E. Savage|title=Models Of Computation: Exploring the Power of Computing|year=1998|publisher=Addison-Wesley|isbn= {{Format ISBN|978-0201895391}}0-201-89539-1}}</ref> The computational complexity of an algorithm can be measured given a model of computation. Using a model allows studying the performance of algorithms independently of the variations that are specific to particular implementations and specific technology.

A wide variety of models are commonly used; some closely resemble the workings of (idealized) conventional computers, while others do not. Some commonly used models are [[register machine]]s, [[random-access machine]]s, [[Turing machine]]s, [[lambda calculus]], [[rewriting system]]s, [[digital circuit]]s, [[cellular automaton|cellular automata]], and [[Petri net]]s.

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{{main|Mechanical computer}}

[[File:De-Te-We-mp3h0651.jpg|thumb|Hamann Manus R, a digital mechanical computercalculator]]

Historically, [[mechanical computer]]s were used in industry before the advent of the [[transistor]].

Mechanical computers retain some interest today, both in research and as analogue computers. Some mechanical computers have a theoretical or didactic relevance, such as [[billiard-ball computer]]s, while hydraulic ones like the [[MONIAC]] or the [[Water integrator]] were used effectively.<ref name=pen-empnew>[[Roger Penrose|Penrose, Roger]]: The Emperor's New Mind. Oxford University Press, 1990. See also corresponding [[The Emperor's New Mind|article on it]].</ref>

While some are actually simulated, others are not{{clarify|vacuous|date=December 2016}}. No attempt is made{{dubious|date=December 2016}} to build a functioning computer through the mechanical collisions of billiard balls. The [[domino computer]] is another theoretically interesting mechanical computing scheme.{{why|date=December 2016}}

===Analog computing===

{{main|analog computer}}

An analog computer is a type of computer that uses ''[[analog signal]]s'', which are continuous physical quantities, to model and solve problems. These signals can be [[Electrical network|electrical]], [[Mechanics|mechanical]], or [[Hydraulics|hydraulic]] in nature. Analog computers were widely used in scientific and industrial applications, and were often faster than digital computers at the time. However, they started to become obsolete in the 1950s and 1960s and are now mostly used in specific applications such as aircraft flight simulators and teaching control systems in universities.<ref name="Johnston">{{cite book | url=https://books.google.com/books?id=iPfU_powAgAC&q=%22through%20the%201980s%22&pg=PA90 | title=Holographic Visions: A History of New Science | publisher=OUP Oxford | author=Johnston, Sean F. | year=2006 | page=90 | isbn={{Format ISBN|978-0191513886}}0-19-151388-6}}</ref> Examples of analog computing devices include [[slide rule]]s, [[nomogram]]s, and complex mechanisms for process control and protective relays.<ref name="9HtsB">{{cite web|url=https://arstechnica.com/information-technology/2014/03/gears-of-war-when-mechanical-analog-computers-ruled-the-waves/|title=Gears of war: When mechanical analog computers ruled the waves|date=2014-03-18|access-date=2017-06-14|archive-url=https://web.archive.org/web/20180908173957/https://arstechnica.com/information-technology/2014/03/gears-of-war-when-mechanical-analog-computers-ruled-the-waves/|archive-date=2018-09-08}}</ref> The [[Antikythera mechanism]], a mechanical device that calculates the positions of planets and the Moon, and the [[planimeter]], a mechanical integrator for calculating the area of an arbitrary 2D shape, are also examples of analog computing.

===Electronic digital computers===

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Most modern computers are electronic computers with the [[Von Neumann architecture]] based on digital electronics, with extensive integration made possible following the invention of the transistor and the scaling of [[Moore's law]].

Unconventional computing is, (according to a{{which|date=Decemberwebsite 2016}}of Center for Nonlinear Studies announcing the conference; descriptionUnconventional Computation:Quo Vadis?, March 21-23 2007 in Santa Fe, New Mexico, USA) <ref>{{cite web | title = Unconventional computation Conference 2007 | url = http://cnls.lanl.gov/uc07/ }}</ref> "an interdisciplinary research area with the main goal to enrich or go beyond the standard models, such as the [[Von Neumann architecture|Von Neumann computer architecture]] and the [[Turing machine]], which have dominated computer science for more than half a century". These methods model their computational operations based on non-standard paradigms, and are currently mostly in the research and development stage.

This computing behavior can be "simulated"{{clarify|date=December 2016}} using classical silicon-based micro-transistors or [[solid state (electronics)|solid state]] computing technologies, but it aims to achieve a new kind of computing.

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=== Neuromorphic quantum computing ===

Neuromorphic Quantum Computing<ref>{{Cite journal |title=Neuromrophic Quantum Computing {{!}} Quromorphic Project {{!}} Fact Sheet {{!}} H2020 |url=https://cordis.europa.eu/project/id/828826 |access-date=2024-03-18 |website=CORDIS {{!}} European Commission |language=en |doi=10.3030/828826|url-access=subscription }}</ref><ref>{{Citation |last1=Pehle |first1=Christian |title=Neuromorphic quantum computing |date=2021-03-30 |arxiv=2005.01533 |last2=Wetterich |first2=Christof|journal=Physical Review E |volume=106 |issue=4 |page=045311 |doi=10.1103/PhysRevE.106.045311 |bibcode=2022PhRvE.106d5311P }}</ref> (abbreviated as 'n.quantum computing') is an unconventional type of computing that uses [[Neuromorphic engineering|neuromorphic computing]] to perform quantum operations. It was suggested that [[Quantum algorithm|quantum algorithms]], which are algorithms that run on a realistic model of [[Quantum computing|quantum computation]], can be computed equally efficiently with neuromorphic quantum computing.<ref>{{Cite journal |last1=Carleo |first1=Giuseppe |last2=Troyer |first2=Matthias |date=2017-02-10 |title=Solving the quantum many-body problem with artificial neural networks |url=https://www.science.org/doi/10.1126/science.aag2302 |journal=Science |language=en |volume=355 |issue=6325 |pages=602–606 |doi=10.1126/science.aag2302 |pmid=28183973 |issn=0036-8075|arxiv=1606.02318 |bibcode=2017Sci...355..602C }}</ref><ref>{{Cite journal |last1=Torlai |first1=Giacomo |last2=Mazzola |first2=Guglielmo |last3=Carrasquilla |first3=Juan |last4=Troyer |first4=Matthias |last5=Melko |first5=Roger |last6=Carleo |first6=Giuseppe |date=2018-02-26 |title=Neural-network quantum state tomography |url=https://www.nature.com/articles/s41567-018-0048-5 |journal=[[Nature Physics]] |language=en |volume=14 |issue=5 |pages=447–450 |doi=10.1038/s41567-018-0048-5 |issn=1745-2481|arxiv=1703.05334 |bibcode=2018NatPh..14..447T }}</ref><ref>{{Cite journal |last1=Sharir |first1=Or |last2=Levine |first2=Yoav |last3=Wies |first3=Noam |last4=Carleo |first4=Giuseppe |last5=Shashua |first5=Amnon |date=2020-01-16 |title=Deep Autoregressive Models for the Efficient Variational Simulation of Many-Body Quantum Systems |url=https://link.aps.org/doi/10.1103/PhysRevLett.124.020503 |journal=Physical Review Letters |volume=124 |issue=2 |page=020503 |doi=10.1103/PhysRevLett.124.020503|pmid=32004039 |arxiv=1902.04057 |bibcode=2020PhRvL.124b0503S }}</ref><ref>{{Citation |last1=Broughton |first1=Michael |title=TensorFlow Quantum: A Software Framework for Quantum Machine Learning |date=2021-08-26 |arxiv=2003.02989 |last2=Verdon |first2=Guillaume |last3=McCourt |first3=Trevor |last4=Martinez |first4=Antonio J. |last5=Yoo |first5=Jae Hyeon |last6=Isakov |first6=Sergei V. |last7=Massey |first7=Philip |last8=Halavati |first8=Ramin |last9=Niu |first9=Murphy Yuezhen}}</ref><ref name="DiVentra2022">{{Citation |last=Di Ventra |first=Massimiliano |title=MemComputing vs. Quantum Computing: some analogies and major differences |date=2022-03-23 |arxiv=2203.12031}}</ref>

Both traditional [[quantum computing]] and neuromorphic quantum computing are physics-based unconventional computing approaches to computations and don't follow the [[von Neumann architecture]]. They both construct a system (a circuit) that represents the physical problem at hand, and then leverage their respective physics properties of the system to seek the "minimum". Neuromorphic quantum computing and [[quantum computing]] share similar physical properties during computation<ref name="DiVentra2022" /><ref>{{Cite journal |last1=Wilkinson |first1=Samuel A. |last2=Hartmann |first2=Michael J. |date=2020-06-08 |title=Superconducting quantum many-body circuits for quantum simulation and computing |journal=Applied Physics Letters |volume=116 |issue=23 |doi=10.1063/5.0008202 |issn=0003-6951|arxiv=2003.08838 |bibcode=2020ApPhL.116w0501W }}</ref>.[[File:Схема криостата МФТИ.jpg|thumb|A quantum computer.]]

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{{main|Microelectromechanical systems|Nanoelectromechanical systems}}

Microelectromechanical systems (MEMS) and nanoelectromechanical systems (NEMS) are technologies that involve the use of microscopic devices with moving parts, ranging in size from micrometers to nanometers. These devices typically consist of a central processing unit (such as an integrated circuit) and several components that interact with their surroundings, such as sensors.<ref>{{cite book|title=Nanocomputers and Swarm Intelligence|vauthors=Waldner JB|publisher=[[ISTE Ltd|ISTE]] [[John Wiley & Sons]]|year=2008|isbn={{Format ISBN|9781848210097}}978-1-84821-009-7|place=London|page=205|author-link=Jean-Baptiste Waldner}}</ref> MEMS and NEMS technology differ from molecular nanotechnology or molecular electronics in that they also consider factors such as surface chemistry and the effects of ambient electromagnetism and fluid dynamics. Applications of these technologies include accelerometers and sensors for detecting chemical substances.<ref name = Ventra2004>{{cite book |title=Introduction to Nanoscale Science and Technology (Nanostructure Science and Technology) |publisher=Springer |___location=Berlin |date=2004|isbn=978-1-4020-7720-3 | url = https://books.google.com/books?id=mccEGiaPEJwC|author1 = Hughes, James E. Jr.|author2 = Ventra, Massimiliano Di |author3 = Evoy, Stephane |author-link2 = Massimiliano Di Ventra}}</ref>

==Chemistry approaches==

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===Peptide computing===

{{main|peptidePeptide computing}}

Peptide computing is a computational model that uses peptides and antibodies to solve NP-complete problems and has been shown to be computationally universal. It offers advantages over DNA computing, such as a larger number of building blocks and more flexible interactions, but has not yet been practically realized due to the limited availability of specific monoclonal antibodies.<ref>{{cite book | doi = 10.1007/3-540-48017-X_27 |author1=M. Sakthi Balan |author2=Kamala Krithivasan|author2-link=Kamala Krithivasan |author3=Y. Sivasubramanyam |title=DNA Computing |chapter=Peptide Computing - Universality and Complexity | volume = 2340 | pages = 290&ndash;299 | url = http://www.csd.uwo.ca/~sakthi/hpp_revised.ps | series = Lecture Notes in Computer Science |date=2002 | isbn = 978-3-540-43775-8 }}</ref><ref>{{cite journal |author1=Hubert Hug |author-link=Hubert Hug |author2=Rainer Schuler |author2-link=Rainer Schuler |name-list-style=amp | year = 2001 | title = Strategies for the development of a peptide computer | journal = Bioinformatics | volume = 17 | issue = 4 | pages = 364&ndash;368 | doi = 10.1093/bioinformatics/17.4.364| pmid=11301306|doi-access = }}</ref>

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===Membrane computing===

{{main|membraneMembrane computing}}

[[File:P-System Membrane Format.pdf|Nine Region Membrane Computer|thumb]]

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===Neuroscience===

{{main|Neuromorphic computing|wetware computer}}

Neuromorphic computing involves using electronic circuits to mimic the neurobiological architectures found in the human nervous system, with the goal of creating artificial neural systems that are inspired by biological ones.<ref>{{Cite journal |last1=Ham |first1=Donhee |last2=Park |first2=Hongkun |last3=Hwang |first3=Sungwoo |last4=Kim |first4=Kinam |title=Neuromorphic electronics based on copying and pasting the brain |url=https://www.nature.com/articles/s41928-021-00646-1 |journal=Nature Electronics |year=2021 |language=en |volume=4 |issue=9 |pages=635–644 |doi=10.1038/s41928-021-00646-1 |s2cid=240580331 |issn=2520-1131|url-access=subscription }}</ref><ref>{{Cite journal |last1=van de Burgt |first1=Yoeri |last2=Lubberman |first2=Ewout |last3=Fuller |first3=Elliot J. |last4=Keene |first4=Scott T. |last5=Faria |first5=Grégorio C. |last6=Agarwal |first6=Sapan |last7=Marinella |first7=Matthew J. |last8=Alec Talin |first8=A. |last9=Salleo |first9=Alberto |date=April 2017 |title=A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing |url=https://www.nature.com/articles/nmat4856 |journal=Nature Materials |language=en |volume=16 |issue=4 |pages=414–418 |doi=10.1038/nmat4856 |pmid=28218920 |bibcode=2017NatMa..16..414V |issn=1476-4660}}</ref> These systems can be implemented using a variety of hardware, such as memristors,<ref name="Maan 1–13">{{Cite journal|last1=Maan|first1=A. K.|last2=Jayadevi|first2=D. A.|last3=James|first3=A. P.|date=2016-01-01|title=A Survey of Memristive Threshold Logic Circuits|journal=IEEE Transactions on Neural Networks and Learning Systems|volume=PP|issue=99|pages=1734–1746|doi=10.1109/TNNLS.2016.2547842|pmid=27164608|issn=2162-237X|arxiv=1604.07121|bibcode=2016arXiv160407121M|s2cid=1798273}}</ref> spintronic memories, and transistors,<ref>{{Cite journal|title = Mott Memory and Neuromorphic Devices|journal = Proceedings of the IEEE|date = 2015-08-01|issn = 0018-9219|pages = 1289–1310|volume = 103|issue = 8|doi = 10.1109/JPROC.2015.2431914|first1 = You|last1 = Zhou|first2 = S.|last2 = Ramanathan|s2cid = 11347598|url=https://zenodo.org/record/895565}}</ref><ref name=":2">{{Cite conference |last1=Alzahrani |first1=Rami A. |last2=Parker |first2= Alice C. |date=2020-07-28 |title=Neuromorphic Circuits With Neural Modulation Enhancing the Information Content of Neural Signaling |conference=International Conference on Neuromorphic Systems 2020|language=EN|doi=10.1145/3407197.3407204|s2cid=220794387|doi-access=free}}</ref> and can be trained using a range of software-based approaches, including error backpropagation<ref>{{cite arXiv |last1=Eshraghian|first1=Jason K.|last2=Ward|first2=Max|last3=Neftci |first3=Emre|last4=Wang|first4=Xinxin|last5=Lenz|first5=Gregor|last6=Dwivedi|first6=Girish|last7=Bennamoun|first7=Mohammed|last8=Jeong|first8=Doo Seok|last9=Lu|first9=Wei D.|title=Training Spiking Neural Networks Using Lessons from Deep Learning |date=1 October 2021 |class=cs.NE |eprint=2109.12894 }}</ref> and canonical learning rules.<ref>{{Cite web |url=https://github.com/Hananel-Hazan/bindsnet | title=Hananel-Hazan/bindsnet: Simulation of spiking neural networks (SNNs) using PyTorch.| website=[[GitHub]]| date=31 March 2020}}</ref> The field of neuromorphic engineering seeks to understand how the design and structure of artificial neural systems affects their computation, representation of information, adaptability, and overall function, with the ultimate aim of creating systems that exhibit similar properties to those found in nature. Wetware computers, which are composed of living neurons, are a conceptual form of neuromorphic computing that has been explored in limited prototypes.<ref name=":1">{{cite web |author=Sincell, Mark |title=Future Tech |work=Discover |url=http://web.archive.org/web/20191120075215 |access-date=2024-03-01}}</ref> Electron microscopy has already been imaging high-resolution anatomical neural connection diagrams,<ref>{{cite journal |last1=Devineni |first1=Anita |title=A complete map of the fruit-fly |journal=Nature |date=October 2024 |volume=634 |page=35 |doi=10.1038/d41586-024-03029-6}}</ref>, and semiconductor chip based intracellular recording at scale can generate physical neural connection maps that specify connection types and strengths,<ref>{{cite journal |last1=Wang |first1=Jun |last2=Jung |first2=Woo-Bin |last3=Gertner |first3=Rona |last4=Park |first4=Hongkun |last5=Ham |first5=Donhee |title=Synaptic connectivity mapping among thousands of neurons via parallelized intracellular recording with a microhole electrode array |journal=Nature Biomedical Engineering |doi=10.1038/s41551-025-01352-5 |url=https://www.nature.com/articles/s41551-025-01352-5|url-access=subscription }}</ref>, and these imaging and recording technologies can inform the neuromorphic system design.

===Cellular automata and amorphous computing===