Unconventional computing: Difference between revisions

Revision as of 09:17, 18 March 2024

Revision as of 10:04, 18 March 2024

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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://everything2.com/index.pl?node_id=1764437 Domino computer]</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>

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=== Neuromorphic quantum computing ===

Neuromorphic Quantum Computing<ref>{{Cite web |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 |last=Pehle |first=Christian |title=Neuromorphic quantum computing |date=2021-03-30 |url=http://arxiv.org/abs/2005.01533 |access-date=2024-03-18 |doi=10.48550/arXiv.2005.01533 |last2=Wetterich |first2=Christof}}</ref> (also known as ‘neuromorphic empowered quantum computing’ or abbreviated as ‘n^quantum computing’) is an [[unconventional computing]] 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 |last=Carleo |first=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 |issn=0036-8075}}</ref><ref>{{Cite journal |last=Torlai |first=Giacomo |last2=Mazzola |first2=Guglielmo |last3=Carrasquilla |first3=Juan |last4=Troyer |first4=Matthias |last5=Melko |first5=Roger |last6=Carleo |first6=Giuseppe |date=2018-0502-26 |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}}</ref><ref>{{Cite journal |last=Sharir |first=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 |pages=020503 |doi=10.1103/PhysRevLett.124.020503}}</ref><ref>{{Citation |last=Broughton |first=Michael |title=TensorFlow Quantum: A Software Framework for Quantum Machine Learning |date=2021-08-26 |url=http://arxiv.org/abs/2003.02989 |access-date=2024-03-18 |doi=10.48550/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>{{Citation |last=Di Ventra |first=Massimiliano |title=MemComputing vs. Quantum Computing: some analogies and major differences |date=2022-03-23 |url=http://arxiv.org/abs/2203.12031 |access-date=2024-03-18 |doi=10.48550/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>{{Citation |last=Di Ventra |first=Massimiliano |title=MemComputing vs. Quantum Computing: some analogies and major differences |date=2022-03-23 |url=http://arxiv.org/abs/2203.12031 |access-date=2024-03-18 |doi=10.48550/arXiv.2203.12031}}</ref><ref>{{Cite journal |last=Wilkinson |first=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}}</ref>.[[File:Схема криостата МФТИ.jpg|thumb|A quantum computer.]]

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[[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=httphttps://psystemsnatcomplab.disco.unimib.it/downloadwp-content/MembIntro2004uploads/sites/94/2023/12/IntroMemb.pdf |accessurl-datestatus=2022-12-30live |archive-datejournal=2011-07-22 |archive-url=https://web.archive.org/web/20110722063157/http://psystems.disco.unimib.it/download/MembIntro2004.pdf |urlarchive-statusdate=dead2011-07-22 |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}}</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 |arxiv=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>

===Cellular automata and amorphous computing===