Light as an Information Carrier: Why Photonic Technology Is the Future of Computational Power

The modern technological landscape is defined by an ever-increasing and accelerating demand for greater computational power. Applications such as artificial intelligence, big data processing, physical simulations, and autonomous computing infrastructures require systems capable of performing complex operations with speed, precision, and high energy efficiency. The historical trajectory of microprocessor advancement relied on the continued miniaturisation of transistors and the growth of circuit integration, in line with the so-called Moore’s Law. However, over the past two decades, the effectiveness of this classical strategy has declined, constrained by the fundamental limits of matter, thermodynamics, and quantum mechanics.

 Photonic technology, the use of light for information transmission and processing, emerges within this context as a fundamentally different computational paradigm. Electronics are based on the motion of charged particles through conductive materials and are therefore subject to resistance-related losses, heat generation, and temporal delays due to capacitive and inductive effects. In contrast, photons—being massless and electrically neutral particles—interact only minimally with the medium through which they propagate. This enables the nearly lossless and ultrafast transmission of information at the speed of light, provided the optical medium is appropriately designed.

The application of photonics in computational architecture offers fundamental advantages. Information can be transmitted with minimal energy consumption and virtually no heat production. Furthermore, the inherent property of light to support multiple wavelengths within a single optical channel—known as wavelength division multiplexing—allows for massive parallel information transfer, which is infeasible at similar densities in electronics. Similarly, photonic systems enable the simultaneous propagation and manipulation of multiple signals without interference, as distinct wavelengths can remain isolated within the same physical path.

A practical photonic computing system requires a complete architecture: highly stable light sources, modulators to encode digital information into optical parameters such as intensity, phase, or polarisation, waveguides to direct the light, switching elements and filters for targeted processing, and detectors for final signal reading. Crucial components like microring resonators allow for the dynamic selection of specific frequencies, functioning as tunable filters or even elementary memory cells under certain conditions of stability and reversibility.

Of particular interest is the ability to execute mathematical operations through the physical propagation of light across structures that act as optical analogues of linear operators. For instance, matrix multiplications can be implemented as transformations of phase and amplitude in waveguide lattices or through interferometric devices. This capability renders photonic systems exceptionally efficient in domains such as neural network training and inference, which are dominated by repetitive large-scale linear operations. Whereas a conventional processor must sequentially carry out memory retrieval, multiplication, and accumulation, a photonic system can achieve the same transformation in a single pass of light through the optical structure.

Despite the documented advantages, the realisation of a fully photonic computer still faces practical and theoretical challenges. While light is an exceptional medium for transmission and modulation, it does not inherently provide mechanisms for persistent storage or state retention equivalent to those in electronic systems. Developing stable optical memory elements, enabling reversible and rewritable storage, and addressing the high thermal and mechanical sensitivity of nano photonic components remain open areas of research. Additionally, the interaction of light with matter requires materials with high refractive indices and low propagation losses, the fabrication of which at nanoscale dimensions is technologically demanding.

Advancements in photonic circuit design have led to solutions compatible with silicon-based manufacturing technologies, making large-scale implementation more realistic. At the same time, progress in non-linear optics and higher-order photonic effects enhances the prospects of creating logic-capable photonic components, potentially replacing classical gates with optical equivalents. Hybrid architectures—combining electronic control and storage with photonic transmission and processing—currently appear to be the most feasible near-term solution.

In summary, photonic computing is not a futuristic promise but a technological evolution grounded in physical and engineering reality. The ability to sustain or even accelerate computational power without increasing energy consumption is crucial for the long-term viability of digital infrastructure, especially in the context of energy scarcity and sustainability imperatives. Light, through its intrinsic physical properties, provides a medium that merges speed, efficiency, parallelism, and reliability. If electronics were the vehicle of 20th-century information systems, photonics is poised to become the foundational mechanism of the 21st. Not as an alternative, but as its natural and necessary progression.

References

1. Miller, D. A. B. (2017). Attojoule Optoelectronics for Low-Energy Information Processing and Communications. Journal of Lightwave Technology, 35(3), 346–396

2. Shastri, B. J., Tait, A. N., Ferreira de Lima, T., Pernice, W. H. P., Bhaskaran, H., Wright, C. D., & Prucnal, P. R. (2021). Photonics for artificial intelligence and neuromorphic computing. Nature Photonics, 15(2), 102–114.

3. Jalali, B., & Fathpour, S. (2006). Silicon photonics. Journal of Lightwave Technology, 24(12), 4600–4615.

4. Sun, C., Wade, M. T., Lee, Y., Orcutt, J. S., Alloatti, L., Georgas, M. S., ... & Stojanović, V. (2015). Single-chip microprocessor that communicates directly using light. Nature, 528(7583), 534–538.

5. Thomson, D., Zilkie, A., Bowers, J. E., Komljenovic, T., Reed, G. T., Vivien, L., ... & Marris-Morini, D. (2016). Roadmap on silicon photonics. Journal of Optics, 18(7), 073003.

6. Bogaerts, W., & Chrostowski, L. (2018). Silicon photonics circuit design: Methods, tools and challenges. Laser & Photonics Reviews, 12(4), 1700237.

7. Notaros, J., Yaacobi, A., Timurdogan, E., & Watts, M. R. (2021). Programmable photonic circuits. Nature, 591(7849), 70–71.