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Reproduction: When Does Adaptation Win? Scaling Laws for Meta-Learning in Quantum Control

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Executive summary
Claim 1: Scaling law for adaptation gain (Theorem 4.8)
Claim 2: Single-qubit X-gate exponential saturation (R^2>0.99, beta~0.083)
Claim 3: Two-qubit CZ under 10x noise: >40pp fidelity gain
Claim 4: Assumption 4.3 Polyak-Lojasiewicz curvature mu~0.03
Claim 5: Corollary 4.10: negligible benefit at low task variance
Claim 6: Classical LQR reproduces the exponential scaling law
Conclusion