Photonics market grew 11% to $11.39 billion in 2026

The integrated photonics market grew to $11.39 billion in 2026, an 11.0% increase from the $10.26 billion recorded in 2025, as limitations in electronic chip scalability drive demand for light-based data transmission. This growth is fueled by expanding fiber optic networks and increasing data center traffic, with the sector promising enhanced performance and energy efficiency. Looking ahead, the market is projected to reach $17.44 billion by 2030, largely driven by exponential increases in data traffic from AI workloads and the growing use of quantum communication systems.

2030 Integrated Photonics Market Growth & 11.2% CAGR Forecast

The integrated photonics market experienced a substantial increase in valuation between 2025 and 2026, rising to $11.39 billion from $10.26 billion, reflecting a strong compound annual growth rate of 11.0%. The past growth has been primarily driven by the rising need for higher bandwidth communication systems, expansion of fiber optic networks, limitations in the scalability of electronic chips, increasing data center traffic and early adoption of silicon photonics technologies.

Beyond the immediate past, projections indicate continued expansion, with the market forecast to reach $17.44 billion by 2030, representing an 11.2% compound annual growth rate. This future trajectory is not simply an extrapolation of current trends, but is predicted to be significantly bolstered by the exponential growth in data traffic generated by artificial intelligence workloads and the concurrent, rapid expansion of hyperscale data centers globally.

The Business Research Company’s report highlights that greater demand for energy-efficient computing designs is also a key factor in this projected growth. Silicon photonics technologies significantly contributed to past market gains, indicating a proven foundation for future innovation. By using light signals on compact, integrated platforms, this technology aims to reduce power consumption while providing scalable solutions for demanding applications in telecommunications, data centers, and sensing.

The report details that advancements in integrated photonics manufacturing processes will further accelerate this growth. The Asia-Pacific region is expected to become the fastest-growing market for integrated photonics, despite North America currently holding the largest share. The Business Research Company’s 2026 market report provides a comprehensive view of global market dynamics, covering regions including South East Asia, Western Europe and the Middle East.

Key enhancements to the report include market attractiveness scoring, total addressable market analysis and updated graphics to provide a detailed understanding of the industry landscape. The fundamental principle behind integrated photonics, using photons instead of electrons for data processing, offers significant advantages in terms of speed and energy efficiency. The report states that “This technology supports ultra-fast and high-capacity data transmission,” emphasizing its potential to meet the ever-increasing demands of data-intensive applications.

Saumya Sahay of The Business Research Company can provide further insight into these findings, and is available for contact at Americas + Asia +44 7882 955267 & +91 8897263534, or Europe +44 7882 955267. The report’s detailed analysis, including company scoring matrices and forecasting dashboards, provides a valuable resource for industry stakeholders seeking to understand the evolving landscape of integrated photonics and capitalize on emerging opportunities.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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