发表机构
The University of Western Australia（西澳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究在Quantinuum H2-1硬件上实现了高效量子态制备与快速验证，通过资源最小化电路和基变换技术，将验证参数降低10个数量级，仅用1000次测量即达0.929保真度。
AI 中文摘要
制备和验证特定量子态是量子设备实现超越经典计算和算法优势的重要能力。在本工作中，我们展示了一个端到端框架，该框架将资源高效的量子态制备与近-term量子硬件上的快速、稳健保真度验证相结合。通过在Quantinuum H2-1 trapped-ion平台上实验制备并验证一个编码数字化声学信号的结构化复杂量子态，我们实现了$F_{\mathrm{hw}} = 0.929$的高硬件保真度。关键的是，这一里程碑的实现并非依赖于理想化假设或深层容错开销，而是通过为NISQ时代和早期容错设备优化的资源最小化电路实现的。此外，我们解决了当前量子态认证中的一个关键限制。虽然像shadow overlap这样的验证方法对随机态效果良好，但对于实际算法中使用的结构化态，其样本复杂度可能变得过高。我们通过引入一种测量前基变换技术来缓解这一问题，该技术将结构化目标的验证参数$\tau$降低了超过10个数量级。这种方法加强了shadow overlap方法的理论认证保证，并将张量网络制备和shadow验证集成到一个统一的工作流程中。这些结果改变了在现实噪声和测量预算下，结构化经典数据如何映射到量子硬件并得到验证的范式。通过将稳健的验证过程压缩到仅1,000次测量射击，该框架提供了一种即时的、可扩展的基准测试标准。
英文摘要
Preparation and verification of specific quantum states is an important capability for quantum devices to realise advantages over classical computations and algorithms. In this work, we have demonstrated an end-to-end framework that combines resource-efficient quantum state preparation with rapid, robust fidelity verification on near-term quantum hardware. By experimentally preparing and validating a structured complex quantum state encoding a digitized acoustic signal on the Quantinuum H2-1 trapped-ion platform, we achieved a high hardware fidelity of $F_{\mathrm{hw}} = 0.929$. Crucially, this milestone was realized without relying on idealized assumptions or deep fault-tolerant overhead, but rather through resource-minimal circuits optimized for NISQ-era and early fault-tolerant devices. Furthermore, we addressed a key limitation in current quantum state certification. While validation methods like shadow overlap work well for random states, their sample complexity can become prohibitively high for the structured states used in practical algorithms. We mitigate this by introducing a pre-measurement basis-change technique that reduces the verification parameter, $τ$, by over 10 orders of magnitude for structured targets. This approach tightens the theoretical certification guarantees of the shadow overlap method and integrates tensor-network preparation and shadow validation into a unified workflow. These results shift the paradigm of how structured classical data can be mapped to and verified on quantum hardware under realistic noise and measurement budgets. By compressing a robust verification procedure to just 1,000 measurement shots, this framework offers an immediate, scalable benchmarking standard.
