Optical Computing: The Basics

By Susha Cheriyedath, M.Sc.Reviewed by Lexie CornerJan 24 2025

Optical computing is a method of computation that uses light or photons, in contrast to traditional approaches that use electrical transistors within semiconductor chips.

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The concept was developed in the early 1980s to perform computations such as the Fourier transform, which are resource-intensive and time-consuming for traditional computing methods. For example, performing a Fast Fourier Transform (FFT) on a 1000x1000 pixel image requires at least a million operations using electrical computers, whereas a simple lens can perform the same task in real time.

Despite its initial promise, interest in optical computing waned due to material limitations that made building practical devices challenging. However, the growing demand for greater computational power has recently reignited research and development in this field.

It is increasingly recognized that optical methods could offer advantages over electrical computation, particularly in the context of the emerging quantum era.¹

Working Principle: Guiding photons

Optical computing works by modulating properties of light, such as intensity, phase, polarization, and wavelength, to encode and process information. For instance, high intensity can represent a binary "1," while low intensity represents "0."

This encoded information is directed through a network of complex interconnected logic gates made from optical elements such as non-linear crystals, modulators, and interferometers. These logic gates perform computations, and the final output is detected by a photon detector.^1-4

A basic optical computation requires four main components: a source, medium, modulator, and detector.

Source

The source encodes data during the initial photon generation stage. For applications requiring a broad spectrum, sources like quantum dots, white-light supercontinuum lasers, and optical parametric oscillators are used. For time-domain quantum computing, short laser pulses are used to enable precise time-tagged outputs.

To develop compact and practical optical computing platforms, vertical-cavity surface-emitting lasers (VCSELs) and micro-light-emitting diodes (mLEDs) are preferred. These sources are highly suitable for integration into miniaturized chips and confined spatial environments.^1-3

Medium

For high-fidelity optical computing, photons must travel with minimal loss. This requires special guiding media with properties like high transparency, significant refractive index contrast, and ease of fabrication. Optical fibers and photonic crystals are commonly used for loss-free signal transport within specific wavelength ranges. In cases where a large bandwidth is needed, free-space propagation is often selected to avoid losses across different wavelengths.^1-3

Modulators

Modulators modify specific optical properties such as intensity, phase, or polarization to construct photon logic gates that enable computational readouts.¹ For intensity modulation, devices like electro-optic, magneto-optic, and acousto-optic amplitude modulators are used.

Non-linear crystals are typically used to perform logic operations involving two photons simultaneously. These crystals are integrated with other modulators such as beamsplitters and interferometers.^1-3

Detectors

Detectors are used to extract optical parameters and convert them into electrical readouts with high accuracy. Chip-based computations often use photodiodes, complementary metal-oxide semiconductors (CMOS), and quantum dot detectors, as these are easy to integrate into spatially constrained designs. The choice of detector depends on factors such as speed, sensitivity, wavelength range, and integration requirements, ensuring efficient and reliable light detection.^1-3

With all these components, arrays of logic gates can be constructed to perform computations based on photon parameters. For example, an AND gate can be created using a beamsplitter and a non-linear medium. Photons from a single source are directed through a waveguide onto a non-linear crystal.^1-3

An upconverted photon is detected by a wavelength-specific detector and interpreted as a "1," while the absence of an upconverted photon is read as "0." The gate produces an output signal only when both photons are superposed on the non-linear crystal, causing a new upconverted photon to be emitted and detected.

In large optical computing platforms, arrays of such logic gates operate in parallel to perform real-world computations.^1-3

What Are the Applications of Optical Computing?

Optical computers offer numerous applications that differ from their electrical counterparts. Fields such as image processing, artificial intelligence (AI), and DNA sequencing generate enormous datasets that overwhelm electrical computers.

Traditional computational machinery struggles with heat dissipation and processing speed, whereas optical computing provides a more efficient alternative. For instance, training large AI models requires sophisticated computational infrastructure with cooling mechanisms to manage the substantial heat generated.^3-8

Optical computing significantly enhances AI model development, particularly large language models requiring parallel training and implementation. This technology allows for much faster processing rates compared to traditional methods.^3-8

Modern data transfer increasingly relies on optical transmission mechanisms. Large quantities of digital data are converted into photons with specific tags and transmitted across the globe. At the receiving end, the optical signal is converted back to an electrical signal to generate useful information. Optical computing could eliminate these transduction processes, thereby improving communication efficiency and reducing costs.^3-8

Quantum computing is another area where optical computing shows promise. Since optical computing utilizes photon properties for calculations, some of which are inherently quantum in nature (such as phase and polarization), integrating quantum computing within an optical computation environment is relatively straightforward. Minor adjustments to sources and detectors can facilitate switching between classical and quantum computing regimes.^3-8

Lastly, optical computing has significant potential in medicine. Imaging techniques like computed tomography (CT) and magnetic resonance imaging (MRI) generate vast amounts of data. These datasets are reconstructed into Fourier-transformed images to extract medical information. Optical computing can perform such Fourier transforms in parallel, enabling real-time disease monitoring and rapid diagnosis.^3-8

Optical Computing vs Electronic Computing: Advantages and Disadvantages

Optical computing offers several advantages over traditional computing methods. Unlike electrical computers that require active cooling media, optical computing generates negligible heat.^3,4

It can handle complex problems more easily through parallel and independent computations, which is impossible with electrical computations at very small spatial regimes due to interference issues. Scaling optical circuits is simpler and more cost-effective than electrical chips, where fabrication constraints often necessitate coupling smaller components.^3,4

Optical circuits can operate at higher speeds, with switching times as fast as a few picoseconds—approximately 1000 times faster than electrical circuits. They also have lower energy requirements, with most energy input needed only for the photon source and detectors, unlike electrical components that require intrinsic voltage bias. Additionally, optical computers are not susceptible to short-circuiting since photons do not interfere with each other.^3,4

Despite these advantages, challenges remain. Optical circuit components are expensive, and integrating optical gates onto a chip is a complex process. The size of optical computing units is still relatively large, making industrial scaling difficult.

Furthermore, optical computing models are highly sensitive to external factors; even a small dust particle can disrupt the computed output. Although optical components have reached the micrometer scale, further miniaturization is essential for broader adoption.^3,4

Nonetheless, progress in optical computing continues, addressing limitations in traditional electronic systems regarding speed, energy efficiency, and scalability. For further technical information on these developments, refer to "Advances in Photonic Devices for Optical Computing" and "Quantum Dot Photonics: A New Horizon in Optical Computing."

References and Further Reading

1. Goswami, D. (2003). Optical computing. Reson. DOI:10.1007/BF02837869, https://link.springer.com/article/10.1007/BF02837869
2. Fu, T., et al. (2024). Optical neural networks: progress and challenges. Light Sci Appl. DOI:10.1038/s41377-024-01590-3, https://www.nature.com/articles/s41377-024-01590-3
3. Li, C., et al. (2021). The challenges of modern computing and new opportunities for optics. PhotoniX. DOI:10.1186/s43074-021-00042-0, https://photonix.springeropen.com/articles/10.1186/s43074-021-00042-0
4. Kibebe, CG., et al. (2024). Harnessing optical advantages in computing: a review of current and future trends. Front. Phys. 12:1379051 DOI:10.3389/fphy.2024.1379051, https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2024.1379051/full
5. Anderson, MG., et al. (2023). Optical Transformers. arXiv:2302. DOI:10.48550/arXiv.2302.10360, https://arxiv.org/abs/2302.10360
6. Shen, Y., et al. (2017). Deep learning with coherent nanophotonic circuits. Nature Photon. DOI:10.1038/nphoton.2017.93, https://www.nature.com/articles/nphoton.2017.93
7. Pile, D. (2024). Optical computing and artificial intelligence. Nat. Photon. DOI:10.1038/s41566-024-01582-0, https://www.nature.com/articles/s41566-024-01582-0
8. Goulet, A., et al. (2002). Simple integration technique to realize parallel optical interconnects: implementation of a pluggable two-dimensional optical data link. Appl. DOI:10.1364/AO.41.005538, https://opg.optica.org/ao/abstract.cfm?URI=ao-41-26-5538 

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