Quantum Security, Quantum Networking

Falqon Systems builds infrastructure for quantum secure networks

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September 15, 2026 by Ivy Delaney

Q*Bird has rebranded as Falqon Systems, signaling a shift from developing quantum communication technologies to building the infrastructure for operational networks. This change reflects the company’s expanded focus from research and development to implementation for secure digital communications.

“The transition to Falqon Systems reflects how our vision has evolved,” says CEO and co-founder Dr. Ingrid Romijn, as the company aims to build infrastructure enabling organizations to securely communicate, collaborate, and build the digital infrastructure for the quantum era, Falqon Systems says. Despite the rebranding, ownership, leadership including Remon Berrevoets and Joshua A. Slater, PhD, customer relationships, and the technology roadmap remain unchanged.

Falqon Systems Rebrands from Q*Bird, Focusing on Network Infrastructure

Falqon Systems now focuses on building the infrastructure for quantum secure networks, a shift from its earlier work developing scalable quantum communication technologies. Falqon Systems aligns its new identity with a broadening technology portfolio, signaling a long-term commitment to trusted digital infrastructure. The company’s strategic evolution positions it to support operational Quantum Secure Networks, a foundation for trusted digital communications, according to Dr. Romijn, and represents an intention to move beyond technology sales toward comprehensive network solutions.

Quantum Secure Networks are becoming the foundation of trusted digital communications.

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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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