Optical computing

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Optical or photonic computing uses photons produced by lasers or diodes for computation. For decades, photons have promised to allow a higher bandwidth than the electrons used in conventional computers.

Most research projects focus on replacing current computer components with optical equivalents, resulting in an optical digital computer system processing binary 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, optoelectronic devices lose 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 lessening the need for electrical power.[1]

Application-specific devices, such as synthetic aperture radar (SAR) and optical 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[edit]

The fundamental building block of modern electronic computers is the transistor. To replace electronic components with optical ones, an equivalent optical transistor is required. This is achieved using materials with a non-linear refractive index. In particular, materials exist[4] 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'[5][6] can be used to create optical logic gates,[6] which in turn are assembled into the higher level components of the computer's CPU. These will be nonlinear optical crystals used to manipulate light beams into controlling other light beams.


There are disagreements between researchers about the future capabilities of optical computers: will they be able to compete with semiconductor-based electronic computers on speed, power consumption, cost, and size? Critics note that[7] real-world logic systems require "logic-level restoration, cascadability, fan-out and 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.

Misconceptions, challenges, and prospects[edit]

A significant challenge to optical computing is that computation is a nonlinear process in which multiple signals must interact. Light, which is an electromagnetic wave, can only interact with another electromagnetic wave in the presence of electrons in a material,[8] 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 is that since light can travel much faster than the drift velocity of electrons, and at frequencies measured in THz, optical transistors should be capable of extremely high frequencies. However, any electromagnetic wave must obey the transform limit, and therefore the rate at which an optical transistor can respond to a signal is still limited by its spectral bandwidth. However, in fiber optic communications, practical limits such as dispersion often constrain channels to bandwidths of 10s of GHz, only slightly better than many silicon transistors. Obtaining dramatically faster operation than electronic transistors would therefore require practical methods of transmitting ultrashort pulses down highly dispersive waveguides.

Photonic logic[edit]

Realization of a Photonic Controlled-NOT Gate for use in Quantum Computing

Photonic logic is the use of photons (light) in logic gates (NOT, AND, OR, NAND, NOR, XOR, XNOR). Switching is obtained using nonlinear optical effects when two or more signals are combined.[6]

Resonators are especially useful in photonic logic, since they allow a build-up of energy from constructive interference, thus enhancing optical nonlinear effects.

Other approaches currently being investigated include photonic logic at a molecular level, using photoluminescent chemicals. In a recent demonstration, Witlicki et al. performed logical operations using molecules and SERS.[9]

Unconventional approaches[edit]

Time delays optical computing[edit]

Basic idea is to delay light (or any other signal) in order to perform useful computations [10]. Of interest would be to solve NP-Complete problems because those are the most difficult problems for the conventional computers.

There are 2 basic properties of light that are actually used in this approach:

  • The light can be delayed by passing it through a 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:

  • First step is to create a graph-like structure made from optical cables and splitters. Each graph has an Start node and an 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 an 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 the that moment, it means that we have no solution for our problem. Otherwise the problem has a solution. Fluctuations can be read with an photodetector and an oscilloscope.

The first problem attacked in this way was the Hamiltonian path problem [10]. Later, [other problems have been tackled in this way].

The simplest one is the Subset sum problem [11]. A optical device solving a instance with 4 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 2 (sub)rays of smaller intensity. These 2 rays will arrive into the second node at moments a1 and 0. Each of them will be divided into 2 subrays which will arrive in the 3rd node at moments 0, a1, a2 and a1 + a2. These represents the all subsets of the set {a1, a2}. We expect fluctuations in the intensity of the signal at no more than 4 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 moment B, it means that we have a solution of the problem, otherwise there is no subset whose sum of elements equals B. For the practical implementation we cannot have zero-length cables, thus all cables are increased with a small (fixed for all) value k. In this case the solution is expected at moment B+n*k.

Wavelength-based computing[edit]

Wavelength-based computing [12] can be used to solve the 3-SAT problem with n variables, m clause and with no more than 3 variables per clause. Each wavelength, contained in a light ray, is considered as possible value-assignments to n variables. The optical device contains prisms and mirrors are used to discriminate proper wavelengths which satisfy the formula.

Computing by xeroxing on transparencies[edit]

This approach uses a Xerox machine and transparent sheets for performing computations [13]. k-SAT problem with n variables, m clauses and at most k variables per clause has been solved in 3 steps:

  • Firstly all 2^n possible assignments of n variables have been generated by performing n xerox copies.
  • Using at most 2k copies 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 all m clauses.

Masking optical beams[edit]

Travelling salesman problem has been solved in [14] 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.

See also[edit]

Photonic molecule


  1. ^ Nolte, D.D. (2001). Mind at Light Speed: A New Kind of Intelligence. Simon and Schuster. p. 34. ISBN 978-0-7432-0501-6. 
  2. ^ Feitelson, Dror G. (1988). "Chapter 3: Optical Image and Signal Processing". Optical Computing: A Survey for Computer Scientists. Cambridge, MA: MIT Press. ISBN 0-262-06112-0. 
  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. doi:10.1364/ol.36.000220. 
  4. ^ https://www.rp-photonics.com/nonlinear_index.html
  5. ^ Jain, K.; Pratt, Jr., G. W. (1976). "Optical transistor". Appl. Phys. Lett. 28 (12): 719. doi:10.1063/1.88627. 
  6. ^ a b c US 4382660, K. Jain & G.W. Pratt, Jr., "Optical transistors and logic circuits embodying the same", published May 10, 1983 
  7. ^ Tucker, R.S. (2010). "The role of optics in computing". Nature Photonics. 4: 405. doi:10.1038/nphoton.2010.162. 
  8. ^ Philip R. Wallace (1996). Paradox Lost: Images of the Quantum. ISBN 0387946594. 
  9. ^ 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. 
  10. ^ a b Mihai Oltean (2006). A light-based device for solving the Hamiltonian path problem. Unconventional Computing. Springer LNCS 4135. pp. 217–227. doi:10.1007/11839132_18. 
  11. ^ Mihai Oltean, Oana Muntean (2009). "Solving the subset-sum problem with a light-based device". Natural Computing. Springer-Verlag. 8 (2): 321–331. doi:10.1007/s11047-007-9059-3. 
  12. ^ Sama Goliaei, Saeed Jalili (2009). An Optical Wavelength-Based Solution to the 3-SAT Problem. Optical SuperComputing Workshop. pp. 77–85. doi:10.1007/978-3-642-10442-8_10. 
  13. ^ Tom Head (2009). Parallel Computing by Xeroxing on Transparencies. Algorithmic Bioprocesses. Springer. pp. 631–637. doi:10.1007/978-3-540-88869-7_31. 
  14. ^ NT Shaked, S Messika, S Dolev, J Rosen (2007). "Optical solution for bounded NP-complete problems". Applied Optics. OSA. 46 (5): 711–724. doi:10.1364/AO.46.000711. 

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