Learning complex quantum processes is a central challenge in many areas of
quantum computing and quantum machine learning, with applications in quantum
benchmarking, cryptanalysis, and variational quantum algorithms. This paper
introduces the first learning framework for studying quantum process learning
within the Quantum Statistical Query (QSQ) model, providing the first formal
definition of statistical queries to quantum processes (QPSQs). The framework
allows us to propose an efficient QPSQ learner for arbitrary quantum processes
accompanied by a provable performance guarantee. We also provide numerical
simulations to demonstrate the efficacy of this algorithm. The practical
relevance of this framework is exemplified through application in
cryptanalysis, highlighting vulnerabilities of Classical-Readout Quantum
Physical Unclonable Functions (CR-QPUFs), addressing an important open question
in the field of quantum hardware security. This work marks a significant step
towards understanding the learnability of quantum processes and shedding light
on their security implications.

本研究论文通过引入学习框架，提供了第一个对量子过程进行学习的 Quantum Statistical Query (QSQ) 模型，定义了量子过程统计查询 (QPSQs)。该框架允许我们提出了一个高效的 QPSQ 学习器，伴随着可证明的性能保证。通过在密码分析中的应用，论文展示了该框架的实际相关性，强调了经典读出量子物理不可复制函数 (CR-QPUFs) 的漏洞，解决了量子硬件安全领域中的一个重要问题。这项工作在理解量子过程的可学习性并揭示其安全影响方面迈出了重要一步。