Quantum Research News, Partnerships

Munich Quantum Valley and Mitacs will fund joint quantum initiatives

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September 16, 2026 by Ivy Delaney

Munich Quantum Valley gGmbH and Mitacs are launching a two-year partnership to bolster quantum research and talent across Canada and Bavaria. The collaboration will fund joint research initiatives with academic institutions, strengthening global networks and encouraging future cooperation in quantum technologies at postsecondary levels.

Stephen Lucas, CEO of Mitacs, says, “Advancing both talent and innovation is key to Canada’s leadership in quantum, which offers a critical opportunity to help grow our country’s future prosperity.” Julia Rucha, Co-Chief Executive Officer of MQV gGmbH, adds that the partnership will facilitate a mutually beneficial exchange of knowledge and talent between Canadian and Bavarian institutions.

Mitacs and Munich Quantum Valley gGmbH Establish Joint Research Programs

This collaboration extends beyond research to encompass workforce development programs, specifically targeting students and researchers within the quantum sector. The agreement signals a shared commitment to innovation, uniting efforts to drive advancements in quantum research, talent, and commercialization. Funding programs will be a key component, supporting joint projects with academic institutions and enhancing the flow of personnel between the two regions; researchers, ideas, and expertise moving freely between institutions are expected to strengthen both regions’ capabilities in the rapidly evolving field of quantum technology.

Advancing both talent and innovation is key to Canada’s leadership in quantum, which offers a critical opportunity to help grow our country’s future prosperity.

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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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