Learning Quantum Processes with Quantum Statistical Queries

• URL: http://arxiv.org/abs/2310.02075v4
• Date: Mon, 05 May 2025 17:08:09 GMT
• Title: Learning Quantum Processes with Quantum Statistical Queries
• Authors: Chirag Wadhwa, Mina Doosti,
• Abstract summary: We initiate the study of learning quantum processes from quantum statistical queries.<n>We present an efficient average-case algorithm along with a nearly matching lower bound with respect to the number of observables to be predicted.<n>We apply our learning algorithm to attack an authentication protocol using Classical-Readout Quantum Physically Unclonable Functions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we initiate the study of learning quantum processes from quantum statistical queries. We focus on two fundamental learning tasks in this new access model: shadow tomography of quantum processes and process tomography with respect to diamond distance. For the former, we present an efficient average-case algorithm along with a nearly matching lower bound with respect to the number of observables to be predicted. For the latter, we present average-case query complexity lower bounds for learning classes of unitaries. We obtain an exponential lower bound for learning unitary 2-designs and a doubly exponential lower bound for Haar-random unitaries. Finally, we demonstrate the practical relevance of our access model by applying our learning algorithm to attack an authentication protocol using Classical-Readout Quantum Physically Unclonable Functions, partially addressing an important open question in quantum hardware security.

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