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Optical computing

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Optical computingorphotonic computinguseslight wavesproduced bylasersor incoherent sources fordata processing,data storage or data communication forcomputing.For decades,photonshave shown promise to enable a higherbandwidththan theelectronsused in conventional computers (seeoptical fibers).

Most research projects focus on replacing current computer components with optical equivalents, resulting in an opticaldigital computersystem processingbinary data.This approach appears to offer the best short-term prospects for commercial optical computing, since optical components could be integrated into traditional computers to produce an optical-electronic hybrid. However,optoelectronicdevices consume 30% of their energy converting electronic energy into photons and back; this conversion also slows the transmission of messages. All-optical computers eliminate the need for optical-electrical-optical (OEO) conversions, thus reducing electricalpower consumption.[1]

Application-specific devices, such assynthetic-aperture radar(SAR) andoptical correlators,have been designed to use the principles of optical computing. Correlators can be used, for example, to detect and track objects,[2]and to classify serial time-domain optical data.[3]

Optical components for binary digital computer

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The fundamental building block of modern electronic computers is thetransistor.To replace electronic components with optical ones, an equivalentoptical transistoris required. This is achieved bycrystal optics(using materials with anon-linear refractive index).[4]In particular, materials exist[5]where the intensity of incoming light affects the intensity of the light transmitted through the material in a similar manner to the current response of a bipolar transistor. Such an optical transistor[6][7]can be used to create opticallogic gates,[7]which in turn are assembled into the higher level components of the computer'scentral processing unit(CPU). These will be nonlinear optical crystals used to manipulate light beams into controlling other light beams.

Like any computing system, an optical computing system needs four things to function well:

  1. optical processor
  2. optical data transfer, e.g. fiber-optic cable
  3. optical storage,[8]
  4. optical power source (light source)

Substituting electrical components will need data format conversion from photons to electrons, which will make the system slower.

Controversy

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There are some disagreements between researchers about the future capabilities of optical computers; whether or not they may be able to compete with semiconductor-based electronic computers in terms of speed, power consumption, cost, and size is an open question. Critics note that[9]real-world logic systems require "logic-level restoration, cascadability,fan-outand input–output isolation ", all of which are currently provided by electronic transistors at low cost, low power, and high speed. For optical logic to be competitive beyond a few niche applications, major breakthroughs in non-linear optical device technology would be required, or perhaps a change in the nature of computing itself.[10]

Misconceptions, challenges, and prospects

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A significant challenge to optical computing is that computation is anonlinearprocess in which multiple signals must interact. Light, which is anelectromagnetic wave,can only interact with another electromagnetic wave in the presence of electrons in a material,[11]and the strength of this interaction is much weaker for electromagnetic waves, such as light, than for the electronic signals in a conventional computer. This may result in the processing elements for an optical computer requiring more power and larger dimensions than those for a conventional electronic computer using transistors.[citation needed]

A further misconception[by whom?]is that since light can travel much faster than thedrift velocityof electrons, and at frequencies measured inTHz,optical transistors should be capable of extremely high frequencies. However, any electromagnetic wave must obey thetransform limit,and therefore the rate at which an optical transistor can respond to a signal is still limited by itsspectral bandwidth.Infiber-optic communications,practical limits such asdispersionoften constrainchannelsto bandwidths of tens of GHz, only slightly better than many silicon transistors. Obtaining dramatically faster operation than electronic transistors would therefore require practical methods of transmittingultrashort pulsesdown highly dispersive waveguides.

Photonic logic

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Realization of a photoniccontrolled-NOT gatefor use in quantum computing

Photonic logic is the use of photons (light) inlogic gates(NOT, AND, OR, NAND, NOR, XOR, XNOR). Switching is obtained usingnonlinear optical effectswhen two or more signals are combined.[7]

Resonatorsare especially useful in photonic logic, since they allow a build-up of energy fromconstructive interference,thus enhancing optical nonlinear effects.

Other approaches that have been investigated include photonic logic at amolecular level,usingphotoluminescentchemicals. In a demonstration, Witlicki et al. performed logical operations using molecules andSERS.[12]

Unconventional approaches

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Time delays optical computing

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The basic idea is to delay light (or any other signal) in order to perform useful computations.[13]Of interest would be to solveNP-complete problemsas those are difficult problems for the conventional computers.

There are two basic properties of light that are actually used in this approach:

  • The light can be delayed by passing it through an optical fiber of a certain length.
  • The light can be split into multiple (sub)rays. This property is also essential because we can evaluate multiple solutions in the same time.

When solving a problem with time-delays the following steps must be followed:

  • The first step is to create a graph-like structure made from optical cables and splitters. Each graph has a start node and a destination node.
  • The light enters through the start node and traverses the graph until it reaches the destination. It is delayed when passing through arcs and divided inside nodes.
  • The light is marked when passing through an arc or through a node so that we can easily identify that fact at the destination node.
  • At the destination node we will wait for a signal (fluctuation in the intensity of the signal) which arrives at a particular moment(s) in time. If there is no signal arriving at that moment, it means that we have no solution for our problem. Otherwise the problem has a solution. Fluctuations can be read with aphotodetectorand anoscilloscope.

The first problem attacked in this way was theHamiltonian path problem.[13]

The simplest one is thesubset sum problem.[14]An optical device solving an instance with four numbers {a1, a2, a3, a4} is depicted below:

Optical device for solving the Subset sum problem

The light will enter in Start node. It will be divided into two (sub)rays of smaller intensity. These two rays will arrive into the second node at momentsa1and 0. Each of them will be divided into two subrays which will arrive in the third node at moments 0,a1,a2anda1 + a2.These represents the all subsets of the set {a1, a2}. We expect fluctuations in the intensity of the signal at no more than four different moments. In the destination node we expect fluctuations at no more than 16 different moments (which are all the subsets of the given). If we have a fluctuation in the target momentB,it means that we have a solution of the problem, otherwise there is no subset whose sum of elements equalsB.For the practical implementation we cannot have zero-length cables, thus all cables are increased with a small (fixed for all) valuek'. In this case the solution is expected at momentB+n×k.

On-Chip Photonic Tensor Cores

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With increasing demands on graphical processing unit-based accelerator technologies, in the second decade of the 21st century, there has been a huge emphasis on the use of on-chip integrated optics to create photonics-based processors. The emergence of both deep learning neural networks based on phase modulation,[15]and more recently amplitude modulation using photonic memories[16]have created a new area of photonic technologies for neuromorphic computing,[17][18]leading to new photonic computing technologies, all on a chip such as the photonic tensor core.[19]

Wavelength-based computing

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Wavelength-based computing[20]can be used to solve the3-SATproblem withnvariables,mclauses and with no more than three variables per clause. Each wavelength, contained in a light ray, is considered as possible value-assignments tonvariables. The optical device contains prisms and mirrors are used to discriminate proper wavelengths which satisfy the formula.[21]

Computing by xero xing on transparencies

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This approach uses a photocopier and transparent sheets for performing computations.[22]k-SAT problemwithnvariables,mclauses and at mostkvariables per clause has been solved in three steps:[23]

  • Firstly all 2npossible assignments ofnvariables have been generated by performingnphotocopies.
  • Using at most 2kcopies of the truth table, each clause is evaluated at every row of the truth table simultaneously.
  • The solution is obtained by making a single copy operation of the overlapped transparencies of allmclauses.

Masking optical beams

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Thetravelling salesman problemhas been solved by Shakedet al.(2007)[24]by using an optical approach. All possible TSP paths have been generated and stored in a binary matrix which was multiplied with another gray-scale vector containing the distances between cities. The multiplication is performed optically by using an optical correlator.

Optical Fourier co-processors

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Many computations, particularly in scientific applications, require frequent use of the 2Ddiscrete Fourier transform(DFT) – for example in solving differential equations describing propagation of waves or transfer of heat. Though modern GPU technologies typically enable high-speed computation of large 2D DFTs, techniques have been developed that can perform continuous Fourier transform optically by utilising the naturalFourier transforming property of lenses.The input is encoded using aliquid crystalspatial light modulatorand the result is measured using a conventional CMOS or CCD image sensor. Such optical architectures can offer superior scaling of computational complexity due to the inherently highly interconnected nature of optical propagation, and have been used to solve 2D heat equations.[25]

Ising machines

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Physical computers whose design was inspired by the theoreticalIsing modelare called Ising machines.[26][27][28]

Yoshihisa Yamamoto's lab atStanfordpioneered building Ising machines using photons. Initially Yamamoto and his colleagues built an Ising machine using lasers, mirrors, and other optical components commonly found on anoptical table.[26][27]

Later a team atHewlett Packard Labsdevelopedphotonic chipdesign tools and used them to build an Ising machine on a single chip, integrating 1,052 optical components on that single chip.[26]

Industry

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Some additional companies involved with optical computing development includeIBM,[29]Microsoft,[30]Procyon Photonics,[31]Lightelligence,[32]Lightmatter,[33]Optalysys,[34]Xanadu Quantum Technologies,QuiX Quantum,ORCA Computing,PsiQuantum,Quandela[fr],andTundraSystems Global.[35]

See also

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References

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  1. ^Nolte, D.D. (2001).Mind at Light Speed: A New Kind of Intelligence.Simon and Schuster. p. 34.ISBN978-0-7432-0501-6.
  2. ^Feitelson, Dror G. (1988). "Chapter 3: Optical Image and Signal Processing".Optical Computing: A Survey for Computer Scientists.Cambridge, Massachusetts: MIT Press.ISBN978-0-262-06112-4.
  3. ^Kim, S. K.; Goda, K.; Fard, A. M.; Jalali, B. (2011). "Optical time-domain analog pattern correlator for high-speed real-time image recognition".Optics Letters.36(2): 220–2.Bibcode:2011OptL...36..220K.doi:10.1364/ol.36.000220.PMID21263506.S2CID15492810.
  4. ^"These Optical Gates Offer Electronic Access - IEEE Spectrum".spectrum.ieee.org.Retrieved2022-12-30.
  5. ^"Encyclopedia of Laser Physics and Technology - nonlinear index, Kerr effect".
  6. ^Jain, K.; Pratt, G. W. Jr. (1976). "Optical transistor".Appl. Phys. Lett.28(12): 719.Bibcode:1976ApPhL..28..719J.doi:10.1063/1.88627.
  7. ^abcUS 4382660,K. Jain & G.W. Pratt, Jr., "Optical transistors and logic circuits embodying the same", published May 10, 1983
  8. ^"Project Silica".Microsoft Research.4 November 2019.Retrieved2019-11-07.
  9. ^Tucker, R.S. (2010)."The role of optics in computing".Nature Photonics.4(7): 405.Bibcode:2010NaPho...4..405T.doi:10.1038/nphoton.2010.162.
  10. ^Rajan, Renju; Babu, Padmanabhan Ramesh; Senthilnathan, Krishnamoorthy."All-Optical Logic Gates Show Promise for Optical Computing".Photonics.Photonics Spectra.Retrieved8 April2018.
  11. ^Philip R. Wallace (1996).Paradox Lost: Images of the Quantum.Springer.ISBN978-0387946597.
  12. ^Witlicki, Edward H.; Johnsen, Carsten; Hansen, Stinne W.; Silverstein, Daniel W.; Bottomley, Vincent J.; Jeppesen, Jan O.; Wong, Eric W.; Jensen, Lasse; Flood, Amar H. (2011)."Molecular Logic Gates Using Surface-Enhanced Raman-Scattered Light".J. Am. Chem. Soc.133(19): 7288–91.doi:10.1021/ja200992x.PMID21510609.
  13. ^abOltean, Mihai (2006).A light-based device for solving the Hamiltonian path problem.Unconventional Computing. Springer LNCS 4135. pp. 217–227.arXiv:0708.1496.doi:10.1007/11839132_18.
  14. ^Mihai Oltean, Oana Muntean (2009). "Solving the subset-sum problem with a light-based device".Natural Computing.8(2): 321–331.arXiv:0708.1964.doi:10.1007/s11047-007-9059-3.S2CID869226.
  15. ^Shen, Yichen; Harris, Nicholas C.; Skirlo, Scott; Prabhu, Mihika; Baehr-Jones, Tom; Hochberg, Michael; Sun, Xin; Zhao, Shijie; Larochelle, Hugo; Englund, Dirk; Soljačić, Marin (July 2017)."Deep learning with coherent nanophotonic circuits".Nature Photonics.11(7): 441–446.arXiv:1610.02365.Bibcode:2017NaPho..11..441S.doi:10.1038/nphoton.2017.93.ISSN1749-4893.S2CID13188174.
  16. ^Ríos, Carlos; Youngblood, Nathan; Cheng, Zengguang; Le Gallo, Manuel; Pernice, Wolfram H. P.; Wright, C. David; Sebastian, Abu; Bhaskaran, Harish (February 2019)."In-memory computing on a photonic platform".Science Advances.5(2): eaau5759.arXiv:1801.06228.Bibcode:2019SciA....5.5759R.doi:10.1126/sciadv.aau5759.ISSN2375-2548.PMC6377270.PMID30793028.
  17. ^Prucnal, Paul R.; Shastri, Bhavin J. (2017-05-08).Neuromorphic Photonics.CRC Press.ISBN978-1-4987-2524-8.
  18. ^Shastri, Bhavin J.; Tait, Alexander N.; Ferreira de Lima, T.; Pernice, Wolfram H. P.; Bhaskaran, Harish; Wright, C. D.; Prucnal, Paul R. (February 2021)."Photonics for artificial intelligence and neuromorphic computing".Nature Photonics.15(2): 102–114.arXiv:2011.00111.Bibcode:2021NaPho..15..102S.doi:10.1038/s41566-020-00754-y.ISSN1749-4893.S2CID256703035.
  19. ^Feldmann, J.; Youngblood, N.; Karpov, M.; Gehring, H.; Li, X.; Stappers, M.; Le Gallo, M.; Fu, X.; Lukashchuk, A.; Raja, A. S.; Liu, J.; Wright, C. D.; Sebastian, A.; Kippenberg, T. J.; Pernice, W. H. P. (January 2021)."Parallel convolutional processing using an integrated photonic tensor core".Nature.589(7840): 52–58.arXiv:2002.00281.Bibcode:2021Natur.589...52F.doi:10.1038/s41586-020-03070-1.hdl:10871/124352.ISSN1476-4687.PMID33408373.S2CID256823189.
  20. ^Sama Goliaei, Saeed Jalili (2009).An Optical Wavelength-Based Solution to the 3-SAT Problem.Optical SuperComputing Workshop. pp. 77–85.Bibcode:2009LNCS.5882...77G.doi:10.1007/978-3-642-10442-8_10.
  21. ^Bartlett, Ben; Dutt, Avik; Fan, Shanhui (2021-12-20)."Deterministic photonic quantum computation in a synthetic time dimension".Optica.8(12): 1515–1523.arXiv:2101.07786.Bibcode:2021Optic...8.1515B.doi:10.1364/OPTICA.424258.ISSN2334-2536.S2CID231639424.
  22. ^Head, Tom (2009).Parallel Computing by Xero xing on Transparencies.Algorithmic Bioprocesses. Springer. pp. 631–637.doi:10.1007/978-3-540-88869-7_31.
  23. ^Computing by xero xing on transparencies,April 21, 2015,retrieved2022-08-14
  24. ^NT Shaked, S Messika, S Dolev, J Rosen (2007). "Optical solution for bounded NP-complete problems".Applied Optics.46(5): 711–724.Bibcode:2007ApOpt..46..711S.doi:10.1364/AO.46.000711.PMID17279159.S2CID17440025.{{cite journal}}:CS1 maint: multiple names: authors list (link)
  25. ^A. J. Macfaden, G. S. D. Gordon, T. D. Wilkinson (2017)."An optical Fourier transform coprocessor with direct phase determination".Scientific Reports.7(1): 13667.Bibcode:2017NatSR...713667M.doi:10.1038/s41598-017-13733-1.PMC5651838.PMID29057903.{{cite journal}}:CS1 maint: multiple names: authors list (link)
  26. ^abcCourtland, Rachel (2 January 2017)."HPE's New Chip Marks a Milestone in Optical Computing".IEEE Spectrum.
  27. ^abCartlidge, Edwin (31 October 2016)."New Ising-machine computers are taken for a spin".Physics World.
  28. ^Cho, Adrian (2016-10-20)."Odd computer zips through knotty tasks".Science.
  29. ^Leprince-Ringuet, Daphne (2021-01-08)."IBM is using light, instead of electricity, to create ultra-fast computing".ZDNET.Retrieved2023-07-02.
  30. ^Wickens, Katie (2023-06-30)."Microsoft's light-based computer marks 'the unravelling of Moore's Law'".PC Gamer.Retrieved2023-07-02.
  31. ^Redrouthu, Sathvik (2022-08-13). "Tensor Algebra on an Optoelectronic Microchip".arXiv:2208.06749[cs.PL].
  32. ^de Wolff, Daniel (2021-06-02)."Accelerating AI at the speed of light".MIT News.Retrieved2023-07-02.
  33. ^Metz, Rachel (19 December 2023)."Photonic Computing Startup Lightmatter Hits $1.2 Billion Valuation".Bloomberg.Retrieved19 December2023.
  34. ^"Optalysys launches FT:X 2000 - The world's first commercial optical processing system".insideHPC.2019-03-07.Retrieved2023-07-02.
  35. ^Gülen, Kerem (2022-12-15)."What Is Optical Computing: How Does It Work, Companies And More".Dataconomy.Retrieved2023-07-02.

Further reading

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