Unconventional computing: Difference between revisions

Revision as of 02:01, 13 April 2024

Revision as of 01:01, 23 April 2024

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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|ISBNisbn= 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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=== Neuromorphic quantum computing ===

Neuromorphic Quantum Computing<ref>{{Cite webjournal |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 |lastlast1=Pehle |firstfirst1=Christian |title=Neuromorphic quantum computing |date=2021-03-30 |url=http://arxiv.org/abs/2005.01533 |access-date=2024-03-18 |doiarxiv=10.48550/arXiv.2005.01533 |last2=Wetterich |first2=Christof}}</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 |lastlast1=Carleo |firstfirst1=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 |lastlast1=Torlai |firstfirst1=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 |lastlast1=Sharir |firstfirst1=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|pmid=32004039 |arxiv=1902.04057 |bibcode=2020PhRvL.124b0503S }}</ref><ref>{{Citation |lastlast1=Broughton |firstfirst1=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 |doiarxiv=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 |doiarxiv=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 |doiarxiv=10.48550/arXiv.2203.12031}}</ref><ref>{{Cite journal |lastlast1=Wilkinson |firstfirst1=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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===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 |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://web.archive.org/web/20191120075215 |access-date=2024-03-01}}</ref>

===Cellular automata and amorphous computing===