Unconventional computing: Difference between revisions

Revision as of 20:43, 14 February 2025

Revision as of 23:05, 14 February 2025

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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}}}}</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|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={{Format 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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===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 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 reconfigurability 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===

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===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|url-status=dead}}</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 ===

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}}</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 |pagespage=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 |url=https://doi.org/10.1063/5.0008202 |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={{Format ISBN|9781848210097}}|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 fields of conductive polymers and organic electronics, which use molecules to affect the bulk properties of materials.

==Biochemistry 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}}</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=02 October 2024 |volume=634 |page=35 |pages=36 |doi=10.1038/d41586-024-03029-6 |url=https://www.nature.com/articles/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}}</ref>, and these imaging and recording technologies can inform the neuromorphic system design.

===Cellular automata and amorphous computing===