Quantum computing has been generating hype for 30 years with promises that it will revolutionize everything from cryptography to drug discovery. Companies like IBM, Google, and IonQ keep announcing quantum computers with more qubits and higher fidelity. But quantum computers still can't solve any real-world problem better than classical computers. This episode examines why, and what the actual barriers are. We start with the physics: quantum computers exploit superposition and entanglement to explore multiple solution paths simultaneously. In theory, this allows them to solve certain problems exponentially faster than classical computers. In practice, quantum systems are incredibly fragile. Qubits lose their quantum properties almost immediately—a problem called decoherence. Error rates are high, and correcting those errors requires adding more qubits, which exacerbates other problems. We trace the actual state of the field: what quantum computers can currently do, what the realistic timeline is for practical quantum advantage, and what problems are even theoretically suited to quantum approaches. The controversial take is that quantum computing might be fundamentally harder than the hype suggests, and that we might be 20 or 30 years away from practical quantum computers that outperform classical systems on real problems. We examine why the hype persists despite the lack of practical progress, and what it would actually take to demonstrate quantum advantage on something that matters.

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