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Information-theoretic generalization bounds for learning from quantum data
Learning tasks play an increasingly prominent role in quantum information and computation.
Lower Bounds on Learning Pauli Channels
In the adaptive setting, we show a lower bound of $\Omega(2^{2. 5n}\epsilon^{-2})$ for $\epsilon=\mathcal{O}(2^{-n})$, and a lower bound of $\Omega(2^{2n}\epsilon^{-2} )$ for any $\epsilon > 0$.
Quantum Differential Privacy: An Information Theory Perspective
While classical computations are essentially noiseless and differential privacy is often achieved by artificially adding noise, near-term quantum computers are inherently noisy and it was observed that this leads to natural differential privacy as a feature.
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