发表机构
Haiqu, Inc.（海奎公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文分析基于样本的Krylov量子对角化在噪声下的资源需求，提出近似编译压缩电路，以减少量子射击和经典子空间维度并恢复收敛。
AI 中文摘要
基于样本的Krylov量子对角化（SKQD）是量子中心超级计算工作流的一个典型范例，它结合了Krylov量子对角化的收敛结构与基于经典采样的后处理。该方法假设重要的计算基组构型能够以足够的概率从一组Krylov态中采样得到，并在此假设下提供收敛保证。我们在退极化噪声下分析了这一假设，推导出射击次数资源估计，揭示了指数级的深度惩罚，并在当前噪声硬件上使用一维单杂质安德森模型（20个格点，40个量子比特）进行了实验。器件噪声破坏了无噪声SKQD分析所预测的实际收敛性，但近似编译技术可以在执行前压缩Krylov时间演化电路。压缩后的电路积累更少的硬件噪声，恢复预期的能量收敛，并同时减少量子射击预算和经典子空间维度，即使在应用经典构型恢复时仍然有益。
英文摘要
Sample-based Krylov quantum diagonalization (SKQD) is a paradigmatic example of a quantum-centric supercomputing workflow that combines the convergence structure of Krylov quantum diagonalization with classical sampling-based post-processing. It provides convergence guarantees assuming that the important computational-basis configurations can be sampled from a set of Krylov states with sufficient probability. We analyze this assumption under depolarizing noise, deriving shot-count resource estimates that expose an exponential depth penalty, and perform experiments on current noisy hardware with the one-dimensional single-impurity Anderson model on a 20-site (40-qubit) instance. Device noise breaks the practical convergence predicted by the noiseless SKQD analysis, but approximate compilation techniques can compress the Krylov time-evolution circuits before execution. The compressed circuits accumulate less hardware noise, recover the expected energy convergence, and reduce both the quantum shot budget and the classical subspace dimension, remaining beneficial even when classical configuration recovery is applied.
CommentsAccepted for the IEEE QCE26 Workshop on Advancing Hybrid Quantum-Classical Computing Through Shared Challenges
