CDT Equity holds 22.7% stake in quantum firm Sarborg

CDT Equity now holds a 22.7 percent stake in Sarborg Limited, a quantum computing firm valued at approximately $709 million following its latest capital raise. Sarborg is applying quantum computation to the complex networks regulating disease genes to improve drug discovery, a challenge that exceeds the capabilities of conventional methods, the company says.

The company recently filed new intellectual property for this work, using quantum sampling to identify potential drug targets within these networks; as Sarborg CEO Dr. Andrew Regan states, “Disease genes are controlled by committees of regulators acting together, a combinatorial layer conventional methods miss, and quantum samplers are built to explore.” This model, applied to 150 disease networks, has already identified promising targets in conditions like Crohn’s disease and COPD.

CDT Equity’s 22.7% Stake Supports Sarborg’s Quantum Computing IP

CDT Equity holds a 22.7 percent stake in Sarborg Limited, comprised of 1,290 shares, establishing a significant ownership position in the quantum computing firm as it expands its intellectual property portfolio. Sarborg focuses its drug discovery strategy on the complex networks regulating disease genes, rather than simulating individual molecules as is common in the field, according to the company. “Much of the quantum sector is still looking for problems that genuinely suit the hardware.

We started from one,” said Dr. Andrew Regan, Chief Executive Officer of Sarborg, highlighting the company’s focus on aligning quantum capabilities with specific biological challenges. Sarborg’s model is designed to function on existing classical computing infrastructure while remaining adaptable for future quantum hardware advancements, a pragmatic approach to realizing near-term benefits. The newly filed intellectual property has been applied to 150 disease networks, including those associated with Crohn’s disease, chronic obstructive pulmonary disease (COPD), and psoriasis.

Analysis of these networks revealed a concentration of combinatorially regulated genes linked to cell-surface receptors and ion channels, both prominent targets for pharmacological intervention. This finding is supported by independent human genetic data across all three conditions. In COPD, the model’s findings aligned approximately eightfold with established drug targets, including CHRM3, the target of the drug tiotropium, demonstrating a strong correlation between the quantum-enhanced analysis and existing pharmacological knowledge.

Sarborg’s quantum-enhanced sampler achieved complete agreement with a known model result in a simulation, a feat that eluded a comparable classical sampler, indicating the potential of the technology to overcome limitations of conventional methods, the firm reports. The company is also actively pursuing a partnership with a leading global quantum computing firm to implement these models on advanced quantum hardware, which aims to further enhance the computational power and efficiency of Sarborg’s approach and potentially accelerate the discovery of novel drug candidates. This latest filing builds upon Sarborg’s July 2026 quantum-enabled PRISM filing in agriculture, expanding its intellectual property estate across its Signature Intelligence models and solidifying its position in the emerging field of quantum biology.

Disease genes are controlled by committees of regulators acting together, a combinatorial layer conventional methods miss, and quantum samplers are built to explore.

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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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