AI 中文总结
本文开发首个将Qiskit电路转换为盲态对应物的开源库，用于盲量子计算。该库按模块化和可重用组件层设计，便于扩展新原语并抵御底层变化影响，还展示了其在盲态变分量子分类器中对鸢尾花数据集的实现。
AI 中文摘要
盲量子计算是一种密码原语，它允许能力有限的客户端将复杂计算委托给远程服务器，而不泄露其数据和/或计算。量子密码学的这一分支已分为两个不同的原语，即量子同态加密（仅涉及数据安全）和通用盲量子计算（涉及数据和计算算法的安全）。这些原语在安全云计算、安全量子变分算法、量子联邦学习和安全多方计算等问题中有广泛应用。然而，尚无用于此类协议快速原型设计的软件工具，阻碍了对潜在应用的学术研究。本文描述了首个此类库的开发，它可将用Qiskit编写的电路转换为其盲态对应物，然后可在客户端-服务器架构中委托执行，而不泄露客户端的数据和/或计算。所提出的库按模块化和可重用组件层设计，便于扩展到新的盲量子计算原语，并能抵御底层原语变化的影响。我们展示了这些原语在用于鸢尾花数据集的盲态变分量子分类器中的实现。
英文摘要
Blind quantum computation is a cryptographic primitive that allows a limited-capability client to delegate its complex computation to a remote server without revealing its data and/or computation. This branch of quantum cryptography has been bifurcated into two distinct primitives, quantum homomorphic encryption (concerning the security of only data) and universal blind quantum computation (concerning the security of data and the computing algorithm). These primitives have immense applicability in problems like secure cloud computing, secure quantum variational algorithms, quantum federated learning, and secure multiparty computation. However, no software tools exist for the rapid prototyping of such protocols, hindering the academic interrogation for potential applications. In this paper, we describe the development of the first such library for transpiling circuits written in Qiskit to its blind counterpart, which can then be delegated in a client-server architecture without revealing the client's data and/or computation. The proposed library is designed in modular and reusable component layers, enabling easier scalability to newer BQC primitives and robustness against changes in underlying primitives. We show the implementation of these primitives to a blind variational quantum classifier for the IRIS dataset.
