Unconventional computing: Difference between revisions

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{{shortShort description|Computing by a wide range of new or unusual methodmethods}}

'''Unconventional computing''' (also known as '''alternative computing''' or '''nonstandard computation''') is [[computing]] by any of a wide range of new or unusual methods. It is also known as '''alternative computing'''.

The term ''unconventional computation'' was coined by [[Cristian S. Calude]] and [[John Casti]] and used at the First International Conference on Unconventional Models of Computation<ref>{{cite web | title = Unconventional Models of Computation 1998 | url = https://www.cs.auckland.ac.nz/research/groups/CDMTCS/conferences/umc98/}}</ref> in 1998.<ref>{{cite web | author = C.S. Calude | title = Unconventional Computing: A Brief Subjective History, CDMTCS Report 480, 2015 | url = https://www.cs.auckland.ac.nz/research/groups/CDMTCS/researchreports/view-publication.php?selected-id=548}}</ref>

==Background==

The general theory of [[computation]] allows for a variety of models.{{clarify|issue="models"methods seems like an undefined technical term or needless jargon this early in article|date=December 2022}}of computation. Computing technology was first developed using [[Machine (mechanical)|mechanical]] systems and then evolved into the use of electronic devices. Other fields of [[modern physics]] provide additional avenues for development.

===ComputationalModels modelof Computation===

{{main|ComputationalModel modelof computation}}

Computational models use computer programs to simulate and study complex systems using an algorithmic or mechanistic approach. They are commonly used to study complex nonlinear systems for which simple analytical solutions are not readily available.<ref>{{Cite web|title=Computational Modeling|url=https://www.nibib.nih.gov/science-education/science-topics/computational-modeling#:~:text=Computational%20modeling%20is%20the%20use,characterize%20the%20system%20being%20studied.|access-date=2021-04-07|website=www.nibib.nih.gov}}</ref> Experimentation with the model is done by adjusting parameters in the computer and studying the differences in the outcome.<ref>{{Cite web|title=Computational models - Latest research and news {{!}} Nature|url=https://www.nature.com/subjects/computational-models|access-date=2021-04-08|website=www.nature.com}}</ref> Operation theories of the model can be derived/deduced from these computational experiments. Examples of computational models include weather forecasting models, earth simulator models, flight simulator models, molecular protein folding models, and neural network models.

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= 978-0-201-89539-1}}</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.

===Mechanical computing===

{{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 | pagespage=90 | isbn=978-01915138860-19-151388-6}}</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|url-status=dead}}</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.

[[File:Kulram.jpg|thumb|An [[abacus]], a type of mechanical 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 | pages=90 | isbn=978-0191513886}}</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|url-status=dead}}</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 the classical silicon-based micro-transistors or [[solid state (electronics)|solid state]] computing technologies, but aimit aims to achieve a new kind of computing.

==Generic approaches==

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[[File:Domino logic gate.jpg|thumb|upright=0.7|An [[OR gate]] built from dominoes]]

A billiard-ball computer is a type of mechanical computer that uses the motion of spherical billiard balls to perform computations. In this model, the wires of a Boolean circuit are represented by paths for the balls to travel on, the presence or absence of a ball on a path encodes the signal on that wire, and gates are simulated by collisions of balls at points where their paths intersect.<ref>{{citation | last1 = Fredkin | first1 = Edward | author1-link = Edward Fredkin | last2 = Toffoli | first2 = Tommaso | author2-link = Tommaso Toffoli | doi = 10.1007/BF01857727 | issue = 3–4 | journal = [[International Journal of Theoretical Physics]] | mr = 657156 | pages = 219–253 | title = Conservative logic | volume = 21 | year =1982 1982|bibcode = 1982IJTP...21..219F | s2cid = 37305161 }}.</ref><ref>{{citation|first=Jérôme|last=Durand-Lose|contribution=Computing inside the billiard ball model|title=Collision-Based Computing|editor-first=Andrew|editor-last=Adamatzky|editor-link=Andrew Adamatzky|publisher=Springer-Verlag|year=2002|pages=135–160|isbn=978-1-4471-0129-1|doi=10.1007/978-1-4471-0129-1_6}}.</ref>

A domino computer is a mechanical computer that uses standing dominoes to represent the amplification or logic gating of digital signals. These constructs can be used to demonstrate digital concepts and can even be used to build simple information processing modules.<ref name="domcom">[http{{Cite web |title=Domino computer - Everything2.com |url=https://everything2.com/index.pl?node_id=1764437 Domino|access-date=2024-05-14 computer]|website=everything2.com}}</ref><ref name=comdomcon>[http://www.pinkandaint.com/oldhome/comp/dominoes/index.html Domino computers] {{webarchive |url=https://web.archive.org/web/20060816075615/http://www.pinkandaint.com/oldhome/comp/dominoes/index.html |date=August 16, 2006 }}, a detailed description written by [http://www.pinkandaint.com/ David Johnston]</ref>

Both billiard-ball computers and domino computers are examples of unconventional computing methods that use physical objects to perform computation.

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===Reservoir computing===

{{main|Reservoir computing}}

Reservoir computing is a computational framework derived from recurrent neural network theory that involves mapping input signals into higher -dimensional computational spaces through the dynamics of a fixed, non-linear system called a reservoir. The reservoir, which can be virtual or physical, is made up of individual non-linear units that are connected in recurrent loops, allowing it to store information. Training is performed only at the readout stage, as the reservoir dynamics are fixed, and this framework allows for the use of naturally available systems, both classical and quantum mechanical, to reduce the effective computational cost. One key benefit of reservoir computing is that it allows for a simple and fast learning algorithm, as well as hardware implementation through [[Reservoir computing#Physical reservoir computers|physical reservoirs]].<ref>{{Cite journal|last1=Tanaka|first1=Gouhei|last2=Yamane|first2=Toshiyuki|last3=Héroux|first3=Jean Benoit|last4=Nakane|first4=Ryosho|last5=Kanazawa|first5=Naoki|last6=Takeda|first6=Seiji|last7=Numata|first7=Hidetoshi|last8=Nakano|first8=Daiju|last9=Hirose|first9=Akira|date=2019-07-01|title=Recent advances in physical reservoir computing: A review|journal=Neural Networks|language=en|volume=115|pages=100–123|doi=10.1016/j.neunet.2019.03.005|pmid=30981085 |issn=0893-6080|doi-access=free|arxiv=1808.04962}}</ref><ref>{{Cite journal|last1=Röhm|first1=André|last2=Lüdge|first2=Kathy|date=2018-08-03|title=Multiplexed networks: reservoir computing with virtual and real nodes|journal=Journal of Physics Communications|volume=2|issue=8|pagespage=085007|bibcode=2018JPhCo...2h5007R|doi=10.1088/2399-6528/aad56d|arxiv=1802.08590 |issn=2399-6528|doi-access=free}}</ref> <br />

===Tangible computing===

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[[File:SandScape.jpg|thumb|upright=0.7|[http://tangible.media.mit.edu/project/sandscape/ SandScape], a tangible computing device installed in the [[Children's Creativity Museum]] in San Francisco]]

Tangible computing refers to the use of physical objects as user interfaces for interacting with digital information. This approach aims to take advantage of the human ability to grasp and manipulate physical objects in order to facilitate collaboration, learning, and design. Characteristics of tangible user interfaces include the coupling of physical representations to underlying digital information and the embodiment of mechanisms for interactive control.<ref>{{cite book |doi=10.1145/1347390.1347392 |chapter=Tangible bits |title=Proceedings of the 2nd international conference on Tangible and embedded interaction - TEI '08 |year=2008 |last1=Ishii |first1=Hiroshi |pagespage=xv |isbn=978-1-60558-004-3 |s2cid=18166868 }}</ref> There are also five defining properties of tangible user interfaces, including the ability to multiplex both input and output in space, concurrent access and manipulation of interface components, strong specific devices, spatially aware computational devices, and spatial re-configurabilityreconfigurability of devices.<ref name="KimMaher2008">{{cite journal |last1=Kim |first1=Mi Jeong |last2=Maher |first2=Mary Lou |title=The Impact of Tangible User Interfaces on Designers' Spatial Cognition |journal=Human–Computer Interaction |date=30 May 2008 |volume=23 |issue=2 |pages=101–137 |doi=10.1080/07370020802016415 |s2cid=1268154 }}</ref>

===Human computing===

{{main|Human computer}}

The term "human computer" refers to individuals who perform mathematical calculations manually, often working in teams and following fixed rules. In the past, teams of people, often women, were employed to perform long and tedious calculations, and the work was divided to be completed in parallel. The term has also been used more recently to describe individuals with exceptional mental arithmetic skills, also known as mental calculators.<ref>{{cite encyclopedia|encyclopedia=Oxford English Dictionary|title=computer|edition=Third|date= March 2008|publisher=Oxford University Press|quote=1613 'R. B.' Yong Mans Gleanings 1, I have read the truest computer of Times, and the best Arithmetician that ever breathed, and he reduceth thy dayes into a short number.}}</ref>

===Human-robot interaction===

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{{main|Swarm robotics|swarm intelligence}}

[[Swarm robotics]] is a field of study that focuses on the coordination and control of multiple robots as a system. Inspired by the emergent behavior observed in social insects, swarm robotics involves the use of relatively simple individual rules to produce complex group behaviors through local communication and interaction with the environment.<ref>{{Cite web|url=http://www.scholarpedia.org/article/Swarm_roboticsjournal|title=Swarm Robotics|last1=Dorigo|first1=Marco|last2=Birattari|first2=Mauro|last3=Brambill|first3=Manuele|date=2014|websitejournal=Scholarpedia|languagevolume=en-UK9 |accessissue=1 |page=1463 |doi=10.4249/scholarpedia.1463 |doi-dateaccess=2022free |bibcode=2014SchpJ...9.1463D |language=en-09-13UK}}</ref> This approach is characterized by the use of large numbers of simple robots and promotes scalability through the use of local communication methods such as radio frequency or infrared.

==Physics approaches==

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[[File:optical-NOT-gate-int.svg|thumb|right|Realization of a photonic controlled-NOT gate for use in quantum computing]]

Optical computing is a type of computing that uses light waves, often produced by lasers or incoherent sources, for data processing, storage, and communication. While this technology has the potential to offer higher bandwidth than traditional computers, which use electrons, optoelectronic devices can consume a significant amount of energy in the process of converting electronic energy to photons and back. All-optical computers aim to eliminate the need for these conversions, leading to reduced electrical power consumption.<ref>{{cite book |first=D.D. |last=Nolte |title=Mind at Light Speed: A New Kind of Intelligence |url=https://books.google.com/books?id=Q9lB-REWP5EC&pg=PA34 |date=2001 |publisher=Simon and Schuster |isbn=978-0-7432-0501-6 |page=34}}</ref> Applications of optical computing include synthetic-aperture radar and optical correlators, which can be used for object detection, tracking, and classification.<ref>{{cite book |title=Optical Computing: A Survey for Computer Scientists |chapter=Chapter 3: Optical Image and Signal Processing |last=Feitelson |first=Dror G. |date=1988 |publisher=MIT Press |___location=Cambridge, Massachusetts |isbn=978-0-262-06112-4 }}</ref><ref>{{cite journal |last1=Kim |first1=S. K. |last2=Goda |first2=K.|last3=Fard |first3=A. M. |last4=Jalali |first4=B.|title= Optical time-___domain analog pattern correlator for high-speed real-time image recognition |journal=Optics Letters |volume=36 |issue=2 |pages=220–2 |date=2011 |doi= 10.1364/ol.36.000220|pmid=21263506 |bibcode=2011OptL...36..220K |s2cid=15492810 |url=https://semanticscholar.org/paper/a32f6fd548f77c47c869d39a84c6a0015c48a562 }}</ref>

===Spintronics===

{{main|Spintronics}}

Spintronics is a field of study that involves the use of the intrinsic spin and magnetic moment of electrons in solid-state devices.<ref>{{Cite journal | last1 = Wolf | first1 = S. A. | last2 = Chtchelkanova | first2 = A. Y. | last3 = Treger | first3 = D. M. | title = Spintronics—A retrospective and perspective | doi = 10.1147/rd.501.0101 | journal = IBM Journal of Research and Development | volume = 50 | pages = 101–110 | year = 2006 }}</ref><ref>{{Cite web|url=http://video.google.com/videoplay?docid=2927943907685656536&q=LevyResearch&ei=dxd1SNCtOqj2rAKxzf1p|title=Physics Profile: "Stu Wolf: True D! Hollywood Story"|access-date=2022-12-30|archive-date=2011-04-18|archive-url=https://web.archive.org/web/20110418015231/http://video.google.com/videoplay?docid=2927943907685656536}}</ref><ref>[https://www.science.org/doi/abs/10.1126/science.1065389 Spintronics: A Spin-Based Electronics Vision for the Future]. Sciencemag.org (16 November 2001). Retrieved on 21 October 2013.</ref> It differs from traditional electronics in that it exploits the spin of electrons as an additional degree of freedom, which has potential applications in data storage and transfer,<ref name="Bhatti et al.">{{cite journal |first1=S. |last1=Bhatti |display-authors=etal |title=Spintronics based random access memory: a review |journal=Materials Today |year=2017 |volume=20 |issue=9 |pages=530–548 |doi=10.1016/j.mattod.2017.07.007|doi-access=free |hdl=10356/146755 |hdl-access=free }}</ref> as well as quantum and neuromorphic computing. Spintronic systems are often created using dilute magnetic semiconductors and Heusler alloys.

===Atomtronics===

{{main|Atomtronics}}

Atomtronics is a form of computing that involves the use of ultra-cold atoms in coherent matter-wave circuits, which can have components similar to those found in electronic or optical systems.<ref>{{Cite journal |last1=Amico |first1=L. |last2=Boshier |first2=M. |last3=Birkl |first3=G. |last4=Minguzzi |first4=A.|author4-link=Anna Minguzzi |last5=Miniatura |first5=C. |last6=Kwek |first6=L.-C. |last7=Aghamalyan |first7=D. |last8=Ahufinger |first8=V. |last9=Anderson |first9=D. |last10=Andrei |first10=N. |last11=Arnold |first11=A. S. |last12=Baker |first12=M. |last13=Bell |first13=T. A. |last14=Bland |first14=T. |last15=Brantut |first15=J. P. |year=2021 |title=Roadmap on Atomtronics: State of the art and perspective |url=https://avs.scitation.org/doi/10.1116/5.0026178 |journal=AVS Quantum Science |language=en |volume=3 |issue=3 |pagespage=039201 |doi=10.1116/5.0026178 |arxiv=2008.04439 |bibcode=2021AVSQS...3c9201A |s2cid=235417597 |issn=2639-0213}}</ref><ref>{{cite journal |last1=Amico |first1=Luigi |last2=Anderson |first2=Dana |last3=Boshier |first3=Malcolm |last4=Brantut |first4=Jean-Philippe |last5=Kwek |first5=Leong-Chuan |last6=Minguzzi |first6=Anna|author6-link=Anna Minguzzi |last7=von Klitzing |first7=Wolf |date=2022-06-14 |title=Colloquium : Atomtronic circuits: From many-body physics to quantum technologies |journal=Reviews of Modern Physics |volume=94 |issue=4 |page=041001 |doi=10.1103/RevModPhys.94.041001 |arxiv=2107.08561 |bibcode=2022RvMP...94d1001A |s2cid=249642063 }}</ref> These circuits have potential applications in several fields, including fundamental physics research and the development of practical devices such as sensors and quantum computers.

===Fluidics===

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{{main|Quantum computing}}

Quantum computing, perhaps the most well-known and developed unconventional computing method, is a type of computation that utilizes the principles of quantum mechanics, such as [[quantum superposition|superposition]] and entanglement, to perform calculations.<ref name="Hidary">{{cite book | last=Hidary | first=Jack | title=Quantum computing : an applied approach | publisher=Springer | publication-place=Cham | date=2019 | isbn=978-3-030-23922-0 | oclc=1117464128 | page=3}}</ref><ref>{{cite book | author1-link= Michael Nielsen| last1=Nielsen |first1=Michael |author2-link = Isaac L. Chuang |last2=Chuang |first2=Isaac |title=[[Quantum Computation and Quantum Information]] |year=2010 |edition=10th anniversary |isbn=978-0-511-99277-3 |oclc= 700706156 |doi=10.1017/CBO9780511976667 | s2cid=59717455 }}</ref> Quantum computers use qubits, which are analogous to classical bits but can exist in multiple states simultaneously, to perform operations. While current quantum computers may not yet outperform classical computers in practical applications, they have the potential to solve certain computational problems, such as integer factorization, significantly faster than classical computers. However, there are several challenges to building practical quantum computers, including the difficulty of maintaining qubits' quantum states and the need for error correction.<ref>{{cite book |doi=10.1007/1-4020-8068-9_8 |chapter=Challenges in Reliable Quantum Computing |title=Nano, Quantum and Molecular Computing |year=2004 |last1=Franklin |first1=Diana |last2=Chong |first2=Frederic T. |pages=247–266 |isbn=1-4020-8067-0 }}</ref><ref>{{cite news |last1=Pakkin |first1=Scott |last2=Coles |first2=Patrick |title=The Problem with Quantum Computers |url=https://blogs.scientificamerican.com/observations/the-problem-with-quantum-computers/ |work=Scientific American |date=10 June 2019}}</ref> Quantum complexity theory is the study of the computational complexity of problems with respect to quantum computers.

=== Neuromorphic quantum computing ===

[[File:Схема криостата МФТИ.jpg|thumb|A quantum computer.]]

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.]]

===Superconducting computing===

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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=9781848210097978-1-84821-009-7|place=London|pagespage=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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{{main|Molecular scale electronics|Chemical computing|Molecular logic gate}}

Molecular computing is an unconventional form of computing that utilizes chemical reactions to perform computations. Data is represented by variations in chemical concentrations,<ref name="ijirt.org">{{cite journal |url=http://www.ijirt.org/paperpublished/IJIRT101166_PAPER.pdf |title=Chemical Computing: The different way of computing|first1=Ambar |last1=Kumar|first2=Akash Kumar | last2 =Mahato| first3=Akashdeep |last3=Singh |journal=International Journal of Innovative Research in Technology |volume =1| issue =6 | issn= 2349-6002|date=2014 |accessdateaccess-date=2015-06-14 |urlarchive-status=dead|archiveurlurl=https://web.archive.org/web/20150615085700/http://www.ijirt.org/paperpublished/IJIRT101166_PAPER.pdf |archivedatearchive-date=2015-06-15 }}</ref> and the goal of this type of computing is to use the smallest stable structures, such as single molecules, as electronic components. This field, also known as chemical computing or reaction-diffusion computing, is distinct from the related fieldfields of conductive polymers and organic electronics, which usesuse molecules to affect the bulk properties of materials.

==Biochemistry approaches==

===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 | year title=DNA 2001Computing | title 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 = free }}</ref>

===DNA computing===

{{main|DNA computing}}

DNA computing is a branch of unconventional computing that uses DNA and molecular biology hardware to perform calculations. It is a form of parallel computing that can solve certain specialized problems faster and more efficiently than traditional electronic computers. While DNA computing does not provide any new capabilities in terms of [[computability theory]], it can perform a high number of parallel computations simultaneously. However, DNA computing has slower processing speeds, and it is more difficult to analyze the results compared to digital computers.

===Membrane computing===

{{main|membraneMembrane computing}}

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

Membrane computing, also known as P systems,<ref>{{cite journalweb |last=Păun |first=Gheorghe |title=Introduction to Membrane Computing |url=https://natcomplab.disco.unimib.it/wp-content/uploads/sites/94/2023/12/IntroMemb.pdf |url-status=live |journal= |archive-url=https://web.archive.org/web/20110722063157/http://psystems.disco.unimib.it/download/MembIntro2004.pdf |archive-date=2011-07-22 |access-date=2022-12-30}}</ref> is a subfield of computer science that studies distributed and parallel computing models based on the structure and function of biological membranes. In these systems, objects such as symbols or strings are processed within compartments defined by membranes, and the communication between compartments and with the external environment plays a critical role in the computation. P systems are hierarchical and can be represented graphically, with rules governing the production, consumption, and movement of objects within and between regions. While these systems have largely remained theoretical,<ref>{{US Patent|20090124506}}</ref> some have been shown to have the potential to solve NP-complete problems and have been proposed as hardware implementations for unconventional computing.

==Biological approaches==

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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 journalconference |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 journalarXiv |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 |arxivclass=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://discovermagazineweb.comarchive.org/2000/octweb/feattech20191120075215 |access-date=20182024-0203-0601}}</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===

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===Ternary computing===

{{main|Ternary computing}}

Ternary computing is a type of computing that uses [[ternary logic]], or base 3, in its calculations rather than the more common [[Principle of bivalence|binary system]]. Ternary computers use trits, or ternary digits, which can be defined in several ways, including unbalanced ternary, fractional unbalanced ternary, balanced ternary, and unknown-state logic. Ternary quantum computers use qutrits instead of trits. Ternary computing has largely been replaced by binary computers, but it has been proposed for use in high -speed, low -power consumption devices using the Josephson junction as a balanced ternary memory cell.

===Reversible computing===