Quantum annealers offer a promising approach to solve Quadratic Unconstrained Binary Optimization (QUBO) problems, which have a wide range of applications. However, when a user submits its QUBO problem to a third-party quantum annealer, the problem itself may disclose the user's private information to the quantum annealing service provider. To mitigate this risk, we introduce a privacy-preserving QUBO framework and propose a novel solution method. Our approach employs a combination of digit-wise splitting and matrix permutation to obfuscate the QUBO problem's model matrix $Q$, effectively concealing the matrix elements. In addition, based on the solution to the obfuscated version of the QUBO problem, we can reconstruct the solution to the original problem with high accuracy. Theoretical analysis and empirical tests confirm the efficacy and efficiency of our proposed technique, demonstrating its potential for preserving user privacy in quantum annealing services.

翻译：量子退火器为求解二次无约束二进制优化问题提供了一种前景广阔的方法，该问题具有广泛的应用。然而，当用户将其QUBO问题提交给第三方量子退火器时，问题本身可能向量子退火服务提供商泄露用户的隐私信息。为降低此风险，我们提出了一种隐私保护的QUBO框架并设计了一种新颖的求解方法。我们的方法结合了按位分割与矩阵置换技术，对QUBO问题的模型矩阵$Q$进行混淆，从而有效隐藏矩阵元素。此外，基于混淆后QUBO问题的解，我们能够以高精度重构原始问题的解。理论分析与实验测试证实了所提技术的有效性与高效性，展现了其在量子退火服务中保护用户隐私的潜力。