Quantum Applications, Quantum Events

Seventy-Five Participants Join LatAm Quantum Water Hackathon

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July 15, 2026 by Ivy Delaney

Seventy-five participants from across Latin America, including Mexico, Colombia, Argentina, Uruguay and Brazil, convened in Puebla, Mexico for the 2026 LatAm Quantum Hackathon, seeking quantum solutions to pressing water challenges. The event tasked teams with optimizing real-world water systems, including the complex water releases from the Falcón International Dam on the Mexico-US border, and modeling the Atoyac river basin. Participants developed quantum approaches to these problems, comparing their results to classical benchmarks using platforms provided by QCentroid. The Open Quantum Institute, which supported the hackathon as part of a global series exploring contributions to the Sustainable Development Goals, asked if quantum computing can help tackle real-world water challenges. Four teams ultimately reached the podium, representing progress toward applying this emerging technology to critical infrastructure.

QCentroid provided the quantum computing platforms used by all participants, representing a direct private sector investment in fostering quantum skills and research within the region. Teams also addressed challenges involving leak detection in hydrographic networks, each developing a quantum approach benchmarked against classical methods. Four teams ultimately reached the podium, with two sharing second place, indicating broad success in applying novel quantum techniques. These participants’ efforts represent progress toward applying quantum solutions to critical infrastructure challenges, and the OQI plans to host additional hackathons in the future.

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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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