Practical Considerations for Processing Massive Classical Data via Quantum Oracle Sketching

2026-09-11

AI Summary

本文探讨了使用量子预言草图处理大规模经典数据的问题，通过将其表示为概率分布并研究样本数、近似误差和更新成本之间的关系，提出了低深度预言合成和交互式量子运行时的需求。

Abstract

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.

Problem

• Classical-to-quantum I/O
• Quantum Oracle Sketching
• data-intensive quantum workloads

Innovation

• Quantum Oracle Sketching
• Classical-to-quantum I/O
• empirical phase oracles
• interactive quantum runtimes
• lower depth oracle synthesis