Singular Photonics gains $2.15M for quantum-enhanced image sensors

Singular Photonics has secured $2.15 million in an oversubscribed funding round, driven by a doubling of sales from 2025 into the first half of 2026. The company develops SPAD-based image sensors and will use the capital to expand engineering capacity and accelerate product development, responding to demand demonstrated by a recent collaboration with instrumentation leader Renishaw. “This has been a phenomenal year for Singular Photonics,” said CEO Shahida Imani. “We’ve already doubled our 2025 sales, we’re approaching break-even, and our customers are telling us exactly what they want from future products.”

$2.15M Funding Fuels SPAD-Based Sensor Development

Singular Photonics secured $2.15 million, led by ACF Investors, with participation from Wren Capital, Cambridge Angels, Scottish Enterprise, Quantum Exponential, and Old College Capital. This capital will accelerate development of a new generation of SPAD-based image sensors, expanding Singular Photonics’ engineering capacity and expediting the delivery of new sensors for applications ranging from machine vision and industrial automation to scientific discovery and medical imaging.

“The industry is moving from capturing images to generating actionable insights directly from light itself, and Singular has both the technology and the team to lead that shift,” said Tim Mills, Managing Partner at ACF Investors. “We’re delighted to back them into their next phase of growth.” Beyond financial backing, Singular Photonics also strengthened its leadership by appointing Dipesh Patel, former CTO of Arm, to its board of directors.

Patel’s 25 years of experience in semiconductor technology positions him to guide the company as it scales its SPAD-based sensor development. “SPADs have long been viewed as highly sensitive photon-counting devices, but Singular is showing that a greater opportunity is now emerging, and I’m looking forward to helping the company capitalize on that opportunity.” Existing shareholders demonstrated their continued commitment by meeting or exceeding their pre-emption rights in the round, indicating belief in the company’s long-term potential, Singular Photonics says.

“In a strong signal of confidence in Singular’s trajectory, existing shareholders followed the new investors into the round,” explained Singular Photonics Chairman Pete Hutton. The company’s sensors differ from conventional image sensors by detecting individual photons and precisely timing their arrival, opening possibilities for imaging systems that capture information previously inaccessible.

This capability, combined with on-chip computation, allows for real-time data analysis at the point of detection, enabling smaller, faster, and more efficient systems suited for edge computing and AI applications. This oversubscribed round means we can respond faster, expanding our sensor portfolio and bringing new features to market on our customers’ timelines. Scottish Enterprise recognizes the potential of Singular Photonics to drive economic growth in Scotland’s photonics sector, noting the company’s role in creating high-value jobs and competing internationally.

Singular Photonics is an exciting addition to our portfolio, combining world-leading sensor science with genuine commercial promise.

Tim Mills, Managing Partner at ACF Investors
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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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