In this episode of The Quantum Computing Podcast with Fexingo, Lucas and Luna explore how quantum computers are being used to simulate climate models more accurately than classical supercomputers. They focus on a specific 2026 breakthrough at the University of Chicago, where a 128-qubit processor modeled atmospheric carbon capture with 40% less error than classical methods. The hosts discuss why quantum's ability to handle entangled variables is crucial for climate science, and how this could accelerate carbon capture technology. They also touch on the challenges of scaling these systems for real-world climate predictions.

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