Episode 45 of The Quantum Computing Podcast tackles the growing challenge of benchmarking quantum algorithms. Lucas and Luna discuss why current metrics like circuit depth and gate fidelity don't tell the full story, and how a consortium of labs is proposing a new standard called Q-score 2.0. They explore a real case from a 2026 cross-platform test involving 100-qubit processors from two different hardware vendors, where Q-score 2.0 revealed performance differences hidden by traditional benchmarks. The episode drills into the specific metric of 'algorithmic error per layer' and why it matters for practical quantum advantage. Hosts also touch on how this connects to the broader push for quantum utility in the NISQ era. A concrete, forward-looking look at what it means to truly measure quantum progress.

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