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Quantinuum to join Cantor Convergence for fintech and quantum talks

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November 10, 2026 by Ivy Delaney

Quantinuum will participate in the 4th Annual Cantor Convergence Conference in Miami, Florida, from November 10-12, 2026, placing the quantum computing company within discussions spanning cryptocurrencies, financial technology, artificial intelligence and energy infrastructure. The company reports achieving industry-leading average two-qubit gate fidelity, a key metric for evaluating quantum processor performance. Quantinuum’s participation reflects its focus on real-world applications of quantum computing, supported by a global workforce of more than 850 employees, including distinguished scientists and researchers.

Quantinuum’s Participation in Cantor Convergence Fintech Conference

The conference, taking place November 10-12, 2026, in Miami, Florida, focuses on the intersection of cryptocurrencies, financial technology, artificial intelligence and energy infrastructure. Company representatives will participate in a fireside chat and conduct individual meetings to discuss potential investment opportunities. The company’s workforce of over 850 employees supports its development of quantum computing platforms designed for practical applications, Quantinuum says. More than 70% of the technology team hold advanced degrees, either doctorates or master’s, demonstrating a substantial investment in specialized expertise.

Quantinuum maintains active collaborations with organizations across pharmaceutical, materials science, financial services, government and industrial sectors, alongside academic and research institutions globally. The company states that it is a leading quantum computing company offering a comprehensive platform designed to make quantum computing deployment possible in real-world environments. Facilities are located across the United States, the United Kingdom, Germany, Japan, Qatar and Singapore, reflecting a global approach to research and development.

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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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