The paper formalizes backend identifiability as hypothesis testing, proves anonymity decays at the Chernoff rate under persistent i.i.d. probing, establishes a utility-anonymity trade-off, and demonstrates 87-100% backend classification on real cloud QPUs.
Experimental quantum-enhanced kernel-based machine learning on a photonic processor.Nature Photonics, 19(9):1020–1027, 2025
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Routing Anonymity and Identifiability of Noisy Quantum Hardware
The paper formalizes backend identifiability as hypothesis testing, proves anonymity decays at the Chernoff rate under persistent i.i.d. probing, establishes a utility-anonymity trade-off, and demonstrates 87-100% backend classification on real cloud QPUs.