Optalysys raises €26.4M to scale photonic FHE chips

Optalysys secures €26.4 million to commercialise photonic encryption hardware

Optalysys, a Leeds-based photonic computing company developing hardware to process data while it remains encrypted, has raised €26.4 million (£23 million) in a Series A extension round. The funding will be used to accelerate commercialisation of the company’s proprietary photonic chips and to support expansion into the United States.

The round was led by Northern Gritstone, with participation from imec.xpand, Lingotto Horizon, and the UK government’s National Security Strategic Investment Fund (NSSIF).

Positioning photonic computing for cloud infrastructure

Dr Nick New, CEO and co-founder of Optalysys, said the financing arrives at a pivotal time for the computing industry as AI and cloud workloads push conventional electronic systems toward physical and economic constraints.

“We are at a defining moment in the evolution of computing. Photonic computing opens up fundamentally new capabilities, allowing data to be moved and processed with far greater speed and efficiency,” Dr Nick New said. “This investment validates both the scale of the opportunity ahead and our ability to execute against it. It allows us to expand into new markets and take an important step towards making photonic computing a mainstream part of cloud infrastructure.”

Founded to make encrypted computation practical

Founded in 2013 by Dr Nick New and CTO Robert Todd, Optalysys focuses on secure computing using light-based processing. The company’s core pitch is that modern systems increasingly need to share and analyse sensitive data across organisations and cloud environments without exposing it in plaintext—a requirement that intensifies as AI adoption expands and data governance tightens.

To address that challenge, the company is building technology designed to accelerate Fully Homomorphic Encryption (FHE), a cryptographic method that enables computation on encrypted data. In practical terms, FHE allows data to remain encrypted while being processed, theoretically reducing the need to decrypt data in third-party environments such as cloud servers.

Why acceleration matters for Fully Homomorphic Encryption (FHE)

While FHE has long been viewed as a breakthrough for privacy-preserving analytics, it is also widely considered computationally expensive. Optalysys argues that making FHE viable at scale requires substantial hardware acceleration—and that conventional electronic computing is nearing performance limits as workloads grow and energy demands rise.

The company’s approach uses optical computing to accelerate FHE beyond what it says is achievable with purely electrical systems. By integrating data movement and processing on a single chip, Optalysys aims to reduce bottlenecks that occur when data is shuttled between memory and compute units, a common constraint in traditional architectures.

Silicon photonics plus digital tech, with an eye on power use

Optalysys says its platform combines silicon photonics with digital technologies to deliver high computational throughput while lowering the carbon footprint associated with energy-intensive computing. The company is developing a programmable, high-density layer intended to run compute-heavy workloads, including GenAI and post-quantum algorithms, as enterprises prepare for both larger AI models and future cryptographic transitions.

Robert Todd, CTO and co-founder, pointed to broader semiconductor industry momentum around photonics as a route to overcome electronic limits in processing capability and power consumption.

“Recent acquisitions in the semiconductor industry have highlighted the role that photonics can play in addressing the limits of electronic computing, particularly in processing capability and power consumption, resulting from the demands of training and running even larger AI models,” Robert Todd said. He added that the company’s approach “uniquely combines data movement and compute within the same package.”

US expansion targets talent and a mature photonics ecosystem

Part of the new capital will support Optalysys’ move into the US, which the company views as a strategic market for photonics and advanced computing. Robert Todd described the expansion as a “natural next step,” citing the concentration of photonics expertise and engineering talent—particularly in Silicon Valley—as key advantages for hiring, partnerships, and go-to-market efforts.

The expansion also signals an intent to engage more directly with hyperscalers, enterprise security buyers, and the broader semiconductor ecosystem, where demand is rising for architectures that can handle AI-era workloads without disproportionate increases in power and cost.

From prototypes to products: LightLocker™ as an early deployment

Optalysys has already begun deploying early forms of its technology through its LightLocker™ Node servers, launched last year. The company describes LightLocker™ as the world’s first dedicated hardware solution designed for encrypted blockchain applications, positioning it as a practical step toward broader adoption of encrypted computation.

With the Series A extension, the company plans to push further from early implementations into wider commercial availability, aiming to prove that photonic acceleration can make FHE workable for real-world cloud and AI use cases.

Investor mix underscores strategic and security interest

The investor syndicate combines venture capital with strategic and public-sector backing. Alongside Northern Gritstone, the participation of imec.xpand reflects interest from the semiconductor research and commercialisation ecosystem, while involvement from the UK’s NSSIF highlights the national security relevance of technologies that strengthen data confidentiality in third-party compute environments.

As AI accelerates data sharing across organisations and borders, companies pursuing privacy-preserving computation are increasingly being watched not only as commercial plays, but also as infrastructure technologies with security and sovereignty implications.

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