Quantum Hardware, High Performance Computing

Former IBM quantum lead takes reins at Q.ANT hardware

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September 9, 2026 by Ivy Delaney

Q.ANT has appointed Joerg Behrend as Vice President Hardware. He brings 15 years of experience at the boundary of hardware and software to oversee the development of the company’s photonic processors. Behrend will lead teams across Q.ANT’s sites in Stuttgart, Germany and Austin, Texas, focusing on scaling the technology from research and development toward commercial production. Q.ANT states, “The harder part starts now: building them reproducibly, at volume, and across multiple generations.” Behrend previously managed up to 30 employees as Senior Manager for IBM Quantum at IBM Research & Development.

Joerg Behrend’s IBM Background Drives Q.ANT Hardware Leadership

Joerg Behrend’s prior experience managing teams of up to 30 employees at IBM Quantum positions him to scale operations at Q.ANT, a company focused on bringing photonic processors to commercial viability. His most recent role at IBM Research & Development in Böblingen involved leading R&D departments focused on cloud backend development, compilers, and runtime engineering for quantum computing, a complex intersection of hardware and software.

This background is particularly relevant as Q.ANT transitions from technology developer to product supplier, a shift requiring robust engineering discipline. Dr. Michael Förtsch, Founder and CEO of Q.ANT, says, “Throughout his career, Joerg Behrend has seen what that takes: engineering hardware and software together until the result is a system a data center can rely on. That discipline is what separates a working prototype from a product.”

Behrend’s arrival at Q.ANT coincides with a broader expansion of the company’s leadership team; Q.ANT has filled five leadership positions within twelve months, including personnel previously at IBM and Intel. This influx of experience from established semiconductor and technology companies signals Q.ANT’s commitment to building a team capable of delivering reliable, high-performance photonic systems. The company’s processors are already in use at the Leibniz Supercomputing Centre, the Jülich Supercomputing Centre, and IONOS, demonstrating initial traction in the scientific computing market, Q.ANT says.

Behrend will oversee the development of future generations of Q.ANT’s Native Processing Units (NPUs), managing teams across both the Stuttgart, Germany, and Austin, Texas, locations. The decision to bring Behrend onboard reflects Q.ANT’s recognition that scaling photonic processor production presents unique challenges.

Bruno Spruth, CTO of Q.ANT and head of the Austin office, emphasizes the importance of strong leadership in navigating this new territory. “Q.ANT is developing an entirely new class of processors.

This requires leaders who can guide complex, cross-location teams through uncharted technical territory, especially as we expand to keep pace with the dynamic growth in the U.S.” With Joerg Behrend, we’ve been able to bring on board the perfect candidate for us. Behrend’s appointment therefore represents a strategic move by Q.ANT to solidify its position in the emerging field of photonic computing and accelerate the transition from research and development to large-scale commercial production.

Photonic processors are running in data centers today. The harder part starts now: building them reproducibly, at volume, and across multiple generations.

Dr. Michael Förtsch, Founder and CEO of Q.ANT
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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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