Deep Tech, Photonic Computing

CollPlant buys LightSolver, enters quantum photonic computing race

Avatar of Ivy Delaney
August 31, 2026 by Ivy Delaney

CollPlant is acquiring LightSolver, an Israeli company that has developed the Laser Processing Unit (LPU), a computing platform combining classical and supercomputing advantages without requiring extreme conditions. LightSolver’s LPU uses the interaction of lasers to perform computation, designed to accelerate large-scale simulations and optimization across industries, the company says. Commercial validation of the LPU is currently underway with Boeing, a top-tier global financial institution, and leading defense companies. The company states that LightSolver is bringing a new computing architecture to the public markets.

CollPlant Acquires LightSolver for Photonic Computing Expansion

This acquisition positions CollPlant to enter the rapidly evolving supercomputing and photonic computing markets, expanding beyond its established focus on regenerative medicine and medical aesthetics. The deal is structured with an equity-centric approach, including upfront shares, pre-funded warrants, and milestone-based warrants tied to LightSolver achieving specific technological and commercial goals, according to the company. Rather than relying on electron flow, the LPU maps mathematical problems onto an optical system, enabling massively parallel computation with potentially exceptional speed and energy efficiency.

This quantum-inspired architecture aims to capture advantages of highly parallel computation without the extreme conditions, like cryogenic cooling, that currently limit broader adoption of quantum computing. The LPU is designed to function as a complementary computing layer alongside existing CPUs and GPUs, targeting computationally intensive workloads in areas such as scientific simulation, engineering, optimization, and Physical AI, where current systems struggle with speed and efficiency.

LightSolver anticipates commercial availability of its LPU platform in 2028, a timeline that differentiates it from many research-stage photonic computing initiatives. Yehiel Tal, Chairperson and Chief Executive Officer of CollPlant, said this acquisition opens a new growth vector for CollPlant in the high-performance photonic computing and supercomputing space. CollPlant remains fully committed to regenerative medicine and medical aesthetics while entering the photonics market, and is actively pursuing strategic collaborations with global leaders interested in advancing, integrating, or leveraging its technology and capabilities.

Dr. Ruti Ben Shlomi, Founder and Chief Executive Officer of LightSolver, believes computing is at an inflection point. She stated that the next great leap will come not from making transistors smaller, but from changing the very medium of computing, from electrons to light, unlocking a new era of speed and energy efficiency, and that her team has turned that idea from fundamental physics into a working machine.

The transaction includes the issuance of approximately 3,734,476 CollPlant shares upfront, alongside pre-funded and milestone-based warrants, and rollover options for existing LightSolver option holders, with shareholder approval required for certain issuances exceeding Nasdaq listing rules.

We believe the next great leap will come not from making transistors smaller, but from changing the very medium of computing – from electrons to light – unlocking a new era of speed and energy efficiency.

Dr. Ruti Ben Shlomi, Founder and Chief Executive Officer of LightSolver
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Avatar of Ivy Delaney

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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