Physical unclonable functions(PUFs) provide a unique fingerprint to a physical entity by exploiting the inherent physical randomness. Gao et al. discussed the vulnerability of most current-day PUFs to sophisticated machine learning-based attacks. We address this problem by integrating classical PUFs and existing quantum communication technology. Specifically, this paper proposes a generic design of provably secure PUFs, called hybrid locked PUFs(HLPUFs), providing a practical solution for securing classical PUFs. An HLPUF uses a classical PUF(CPUF), and encodes the output into non-orthogonal quantum states to hide the outcomes of the underlying CPUF from any adversary. Here we introduce a quantum lock to protect the HLPUFs from any general adversaries. The indistinguishability property of the non-orthogonal quantum states, together with the quantum lockdown technique prevents the adversary from accessing the outcome of the CPUFs. Moreover, we show that by exploiting non-classical properties of quantum states, the HLPUF allows the server to reuse the challenge-response pairs for further client authentication. This result provides an efficient solution for running PUF-based client authentication for an extended period while maintaining a small-sized challenge-response pairs database on the server side. Later, we support our theoretical contributions by instantiating the HLPUFs design using accessible real-world CPUFs. We use the optimal classical machine-learning attacks to forge both the CPUFs and HLPUFs, and we certify the security gap in our numerical simulation for construction which is ready for implementation.

翻译：物理不可克隆函数（PUF）通过利用物理实体固有的随机性，为其提供独特的指纹。Gao等人指出，当前大多数PUF易受基于机器学习的复杂攻击。我们通过整合经典PUF与现有量子通信技术来解决这一问题。具体而言，本文提出了一种可证明安全的通用PUF设计，称为混合锁定PUF（HLPUF），为经典PUF的安全性提供了实用的解决方案。HLPUF使用经典PUF（CPUF），并将其输出编码为非正交量子态，以隐藏底层CPUF的输出结果，使其不被任何对手获取。在此，我们引入量子锁以保护HLPUF免受任何通用对手的攻击。非正交量子态的不可区分性，结合量子锁定技术，阻止了对手访问CPUF的输出结果。此外，我们证明，通过利用量子态的非经典特性，HLPUF允许服务器重用挑战-响应对以进行进一步的客户端认证。这一结果为在服务器端维护小规模挑战-响应对数据库的同时，长期运行基于PUF的客户端认证提供了高效解决方案。随后，我们通过使用实际可用的CPUF实例化HLPUF设计，支持了理论贡献。我们采用最优的经典机器学习攻击来伪造CPUF与HLPUF，并通过数值模拟验证了拟实现构造的安全差距。