处理大规模经典数据时量子预言草图的实际考量
Practical Considerations for Processing Massive Classical Data via Quantum Oracle Sketching
- KTH Royal Institute of Technology（皇家理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文针对量子预言草图处理大规模经典数据的实际需求，提出离线实现并利用DNA指纹识别验证，发现电路过深，需低深度合成与交互式运行时。
AI中文摘要：
经典到量子的输入/输出是数据密集型量子工作负载的关键瓶颈。量子预言草图（QOS）已被提出以解决此问题。我们识别了其实际需求，包括将数据表示为预言机地址上的概率分布、样本数量、近似误差与更新成本之间的关系，以及在线执行所需的相干性和运行时支持。为了在当前量子计算设备上研究QOS，我们开发了一种离线实现，将样本块编译为经验相位预言机，并使用DNA指纹识别进行评估。该应用在相对较少的样本下收敛，而精确相位融合将重复更新合并到同一地址。然而，编译后的电路对于当前量子计算设备而言仍然过深。因此，实际的QOS需要更低深度的预言机合成和交互式量子运行时。
英文摘要：
Classical-to-quantum I/O is a critical bottleneck for data-intensive quantum workloads. Quantum Oracle Sketching (QOS) has been proposed to address this problem. We identify its practical requirements, including the representation of data as a probability distribution over oracle addresses, the relation between sample count, approximation error, and update cost, and the coherence and runtime support required for online execution. To study QOS on current quantum computing devices, we develop an offline realization that compiles sample blocks into empirical phase oracles and evaluate it using DNA fingerprinting. The application converges with relatively few samples, while exact phase fusion combines repeated updates to the same address. However, the compiled circuits remain too deep for current quantum computing devices. Practical QOS therefore requires lower depth oracle synthesis and interactive quantum runtimes.
