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The exponential sample-complexity separation for Pauli-channel learning (Chen-Cotler-Huang-Li / Huang Science 2022), surfaced as a first-class quantum-ML capability: estimating an n-qubit channel's 2^n fidelities to accuracy epsilon needs Omega(2^n) copies WITHOUT quantum memory but O(1/eps^2) n-independent copies WITH a two-copy Bell measurement. Both estimators simulated exactly (deterministic in seed); the demo shows Bell resolving all fidelities in a fixed copy budget while the entanglement-free strategy is starved. A data-access advantage that survives dequantization — and exactly why noise_learn's Bell/CB sampling is data-efficient. wai-web QDataSeparation; live /quantum-ml panel tying the separation to the deployed wai.quantum.cal.noise_learn (verified receipt + byte-exact model hash). Co-Authored-By: Claude Opus 4.8 <[email protected]> |
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| cbindgen.toml | ||