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Quantum B wins European patent for secure data sharing with entanglement

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September 16, 2026 by Ivy Delaney

European Patent EP 4462727 has been granted to Quantum B for a protocol that merges quantum key agreement with a novel method for achieving data consensus in distributed systems like blockchain networks, the company says. The technology addresses a critical challenge: allowing multiple independent computers to reliably agree on shared data, even with limited trust.

As co-inventor Miriam Kosik explains, the team investigated whether quantum entanglement could “simplify multiple participants agreeing on a result simultaneously.” This patent builds on research originally conducted when the company was known as Quantum Blockchains, explicitly linking early work in quantum cryptography and blockchain technology.

EP 4462727: Quantum Conference Key Agreement for Distributed Systems

Network participants perform measurements and exchange information, verifying data consistency through quantum phenomena. The patent application details a solution where participants check data block consistency via exchanged information, establishing agreement through quantum measurement results.

Mirek Sopek, CEO of Quantum B, confirmed the team continues to refine the protocol and explore new approaches to distributed consensus, and further results are expected as development progresses, according to the company. Quantum B is also currently developing a portfolio of quantum and post-quantum solutions, including its pQKD hardware platform and participation in the POSEIDON project for post-quantum security in European digital identity platforms.

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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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