As quantum computing advances, quantum circuit simulators serve as critical tools to bridge the current gap caused by limited quantum hardware availability. These simulators are typically deployed on cloud platforms, where users submit proprietary circuit designs for simulation. In this work, we demonstrate a novel timing side-channel attack targeting cloud-based quantum simulators. A co-located malicious process can observe fine-grained execution timing patterns to extract sensitive information about concurrently running quantum circuits. We systematically analyze simulator behavior using the QASMBench benchmark suite, profiling timing and memory characteristics across various circuit executions. Our experimental results show that timing profiles exhibit circuit-dependent patterns that can be effectively classified using pattern recognition techniques, enabling the adversary to infer circuit identities and compromise user confidentiality. We were able to achieve 88% to 99.9% identification rate of quantum circuits based on different datasets. This work highlights previously unexplored security risks in quantum simulation environments and calls for stronger isolation mechanisms to protect user workloads

翻译：随着量子计算的发展，量子电路模拟器成为弥补当前量子硬件资源有限所造成鸿沟的关键工具。这些模拟器通常部署在云平台上，用户可提交专有电路设计进行模拟。本研究展示了一种针对云端量子模拟器的新型时序侧信道攻击。通过共置的恶意进程观测细粒度执行时序特征，攻击者能够提取并行运行的量子电路的敏感信息。我们使用QASMBench基准测试套件系统分析模拟器行为，剖析不同电路执行过程中的时序与内存特征。实验结果表明，时序特征呈现电路依赖性模式，利用模式识别技术可对其进行有效分类，使攻击者能够推断电路身份并破坏用户机密性。基于不同数据集，我们实现了88%至99.9%的量子电路识别率。这项工作揭示了量子模拟环境中先前未被探索的安全风险，并呼吁建立更强的隔离机制以保护用户工作负载。