The concept of using light for computation has existed for many years, yet electronic computing has long dominated because it scaled more easily, delivered consistent performance, and remained cost-efficient. That long-standing advantage is beginning to erode. The explosive rise of artificial intelligence has brought energy efficiency to the forefront, as data movement and large-scale mathematical operations now drive both power consumption and hardware costs. Optical computing is emerging as a promising alternative: light can naturally handle high data rates and parallel operations while dissipating far less energy than conventional electronic systems, as highlighted in a recent Technology Feature by Neil Savage.
Despite its appeal, turning optical computing into a practical reality is far from straightforward. One of the main obstacles lies in precisely controlling, modulating, and coupling light at scales far below its wavelength, and doing so consistently across large chip areas. Advances in nanostructured materials - such as photonic crystals, plasmonic structures, quantum dots, and metasurfaces - are beginning to overcome these limitations by compressing essential optical functions down to the nanoscale and enabling on-chip integration.
Among these approaches, photonic crystals represent one of the most established successes in nanophotonics. Early progress was constrained by fabrication challenges, as creating periodic features comparable to optical wavelengths was extremely difficult. This changed in the early 2000s with improvements in nanofabrication techniques, allowing precise control over light propagation and interference. While photonic crystals are not the dominant platform for optical computing - where industry currently favors Mach - Zehnder interferometers and ring resonators - they have found strong commercial adoption in photonic-crystal surface-emitting lasers. Companies such as Vector Photonics have demonstrated compact, planar laser designs that emit high-quality beams and integrate efficiently at high densities, making them attractive light sources for future photonic processors.
Plasmonics takes miniaturization even further. By confining electromagnetic fields at metal - dielectric boundaries, plasmonic devices enable optical switching and modulation at length scales of just tens to hundreds of nanometers - far smaller than most other photonic technologies. This extreme confinement allows ultrafast electro-optic modulation that surpasses the performance limits of traditional silicon photonics. However, practical challenges remain significant, including optical losses, heat accumulation at metal interfaces, and variability across large-scale fabrication. The success of plasmonic neural networks will likely depend more on solving these engineering issues than on uncovering new physical principles.
Quantum dots and other quantum-confined materials contribute a different advantage: compact, energy-efficient light generation and optical gain that can be directly integrated with silicon platforms. Their near-term impact is most visible in optical interconnects, where companies such as Quintessent are developing quantum-dot lasers for data centers and accelerated computing systems. Beyond light generation, quantum dots also enable strong and tunable optical nonlinearities, potentially allowing information processing at extremely low photon counts and opening pathways toward future optical and quantum computing architectures.
Metasurfaces represent perhaps the most disruptive application of nanoscience in optical computing. In these systems, arrays of carefully engineered subwavelength resonators perform mathematical operations as light passes through them, effectively embedding computation within the material itself. This idea is rapidly transitioning from theory to hardware. For example, Neurophos is developing metasurface-based optical modulators that are dramatically smaller - by orders of magnitude - than conventional silicon photonic components, with a focus on optical matrix multiplication. Recent demonstrations of optical neural networks show that metasurfaces can replace bulky free-space optical elements with compact, chip-scale solutions, simplifying system design and significantly improving energy efficiency.
Beyond reducing device size, nanoscience introduces entirely new computing paradigms. Emerging nanomaterials - including phase-change materials, magneto-optical systems, and ferroic compounds - enable memory, reconfigurability, and non-volatile behavior directly within optical circuits. Phase-change materials such as Ge - Sb - Te and Ge - Sb - Se - Te, already widely used in electronic memory, can reversibly alter their optical properties in response to short electrical or optical pulses. Experimental photonic systems have shown that these reversible changes can directly encode synaptic weights and learning rules, reducing the need for external electronic control.
Ultimately, the future of optical computing depends not only on innovative materials and device concepts, but also on the ability to manufacture them reliably and at scale. Progress in nanofabrication is therefore just as critical as advances in optical physics. This moment mirrors the evolution of silicon-on-insulator technology in the early 1990s, when breakthroughs such as ion-slicing wafer bonding enabled laboratory ideas to become commercially viable. Recent progress in lithium tantalate on insulator photonics suggests that a similar transition may now be underway.
As emphasized in the Technology Feature, optical computing is unlikely to replace CMOS electronics outright. Instead, it will complement electronic systems in applications where bandwidth and energy efficiency impose fundamental limits. From the perspective of Nature Nanotechnology, this hybrid future places materials discovery, nanoscale engineering, advanced fabrication, and photonic - electronic integration at the heart of the next era of computing.
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