In this episode, Lucas and Luna explore how quantum computing is transforming portfolio risk management. They discuss a specific case: how JPMorgan Chase is experimenting with quantum algorithms to run Value-at-Risk simulations that would take classical supercomputers days. The conversation covers the limitations of classical Monte Carlo methods, the promise of amplitude estimation, and the practical hurdles—like error rates and qubit coherence times—that still need to be solved. Lucas explains that a 50-qubit quantum computer with gate fidelities above 99.9 percent could already provide a speedup for certain risk calculations, but today's hardware is still a few years away. They also touch on the role of hybrid classical-quantum models and what it means for financial institutions planning their quantum strategy. If today's tech insight was worth a coffee to you, consider supporting the show at buy me a coffee dot com slash fexingo.

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