Xiphera’s new chip builds a secure foundation with post-quantum crypto

Xiphera launched nQrux Root of Trust on September 30, 2026, delivering a new hardware-based foundation for secure systems built with post-quantum cryptography. The intellectual property is designed for direct integration into custom application-specific integrated circuits and systems on chips, offering a streamlined path to enhanced security. Originally developed through an ESA General Support Technology Programme project for demanding space applications, the technology now extends to a wider range of industries. “Space systems place high demands on security and reliability, sometimes with missions having very long-term operation,” says Petri Jehkonen, Director of Strategic Programs at Xiphera.

nQrux Root of Trust: Secure Foundation for ASIC and SoC

The nQrux Root of Trust, launched on September 30, 2026, establishes device identity and safeguards cryptographic keys through a purely digital logic implementation, eliminating the vulnerabilities associated with CPU or firmware dependencies. This architecture minimizes the potential attack surface by maintaining a simplified design, a critical consideration for applications demanding high security. The project focused on creating quantum-resilient hardware security, a necessity given the increasing threat of quantum computing to current encryption standards.

Xiphera’s new chip builds a secure foundation with post-quantum crypto
Xiphera: nQrux® Root of Trust launch — Source: xiphera.com

This initial work has now expanded to serve a broader range of applications, including IoT, industrial automation and data centers. The compact implementation of nQrux Root of Trust reduces both logic and memory requirements, resulting in a smaller silicon footprint while simultaneously enabling quantum-resilient security. This efficiency is particularly valuable in resource-constrained environments, such as edge devices and space-based systems.

The IP combines secure device identity, key management and modern cryptography, including Post-Quantum Cryptography, to provide a comprehensive security solution. Matti Tommiska, CEO and Co-founder of Xiphera, explains, “Root of Trust is an important addition to our portfolio. It combines our expertise in hardware-based cryptography into a solution that customers can integrate directly into their designs.” This gives them a faster way to build a secure foundation for their products, while allowing their R&D teams to focus on their own product development and innovation.

The design of nQrux Root of Trust prioritizes ease of integration into existing ASIC, SoC, and FPGA architectures. By using a ready-to-integrate IP core, developers can significantly reduce development effort and lead times compared to building these security functions from scratch, allowing companies to bring secure products to market more quickly and gain a competitive advantage in an increasingly security-conscious landscape.

The IP’s suitability extends to applications spanning AI infrastructure, defense systems and mission-critical operations, highlighting its versatility and broad applicability, Xiphera says. The company anticipates that this approach will become increasingly important as the need for hardware-level security continues to grow across diverse industries.

Root of Trust is an important addition to our portfolio. It combines our expertise in hardware-based cryptography into a solution that customers can integrate directly into their designs. This gives them a faster way to build a secure foundation for their products, while allowing their R&D teams to focus on their own product development and innovation.

Matti Tommiska, CEO and Co-founder of Xiphera
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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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