Episode 30 of Quantum Computing Business with Fexingo explores a surprising new frontier: financial modeling. Lucas and Luna dive into how quantum algorithms are starting to tackle portfolio optimization, risk calculation, and derivatives pricing — problems that chew up massive classical compute time. The episode centers on a concrete case: a 2025 pilot by a major European bank that used a superconducting quantum processor to run a multi-asset value-at-risk calculation 100 times faster than a classical supercomputer, though with limited accuracy. Lucas explains why quantum's advantage in sampling high-dimensional probability distributions is a natural fit for finance. Luna asks about the elephant in the room — error rates and near-term practicality. They discuss the hybrid classical-quantum approach that's gaining traction, and why the first production quantum applications might appear in investment banks before pharmaceutical labs. No hype, just a clear-eyed look at where quantum and Wall Street are starting to intersect.

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