PHIX opens US photonics packaging facility in Montana

Photo: ADRIAN SANCHEZ-GONZALEZ · phix.com

On October 7, PHIX opened its new photonics packaging facility in Bozeman, Montana, with a ribbon-cutting ceremony attended by PHIX CEO Albert Hasper, Jose Pozo of Optica and Montana officials. The facility is designed to bolster Montana’s Integrated Photonics Ecosystem (IPE) by adding packaging and assembly capabilities to existing strengths in photonic integrated circuit design, development, and testing.

“This facility gives us a platform to expand our U.S. presence and support companies working to bring new photonic technologies to the market,” said Albert Hasper, Chief Executive Officer at PHIX Photonics Assembly. Jason Yager, Executive Director of the Montana Photonics & Quantum Alliance (MPQA), joined Hasper and Commissioner of Industry Sarah Swanson at the ceremony.

PHIX Facility Expands Montana’s Integrated Photonics Ecosystem (IPE)

The addition is intended to support companies commercializing PIC-based products within the state’s Integrated Photonics Ecosystem. The Bozeman location positions PHIX closer to its North American customer base and complements existing regional expertise, according to the company. Montana’s combination of industry, research and public investment in photonics is furthered by this facility, which adds packaging and assembly to the state’s capabilities. By providing a more complete service offering to companies developing and deploying photonic technologies throughout the United States, PHIX aims to support the growth of the field.

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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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