AI 中文总结
研究利用子空间量子对角化的鲁棒性特性，通过基于梯度的算子剪枝和克利福德舍入两种互补技术压缩电路，应用于VQE假设。对21个分子消融研究及6个分子硬件验证表明，能在保证精度下大幅压缩电路、提升模拟速度。
AI 中文摘要
子空间量子对角化（SQD）通过在量子样本所跨越的子空间中对哈密顿量进行经典对角化来恢复基态能量，只需要具有足够基态重叠的比特串，而不是精确的变分能量。我们揭示并利用了这种未被充分探索的鲁棒性特性：在SQD精度下降之前，可以从采样电路中去除多少非克利福德和变分表现力？我们通过两种互补的压缩技术来回答：基于梯度的算子剪枝，它丢弃影响小的激发算子；以及克利福德舍入，它将剩余参数舍入到最接近的克利福德角。这两种技术都可以应用于简化量子比特哈密顿量的变分量子本征求解器（VQE）假设。对21个分子进行的系统消融研究表明，即使在两个轴上压缩50%，SQD误差中位数仍保持在化学精度范围内，而模拟加速达到33倍。在IBM量子硬件上对6个分子进行的硬件验证证实，转译深度最多可减少2.8倍，且SQD精度无损失。
英文摘要
Sample-based Quantum Diagonalization (SQD) recovers ground-state energies by classically diagonalizing a Hamiltonian in the subspace spanned by quantum samples, requiring only bitstrings with sufficient ground-state overlap rather than an accurate variational energy. We reveal and exploit this underexplored robustness property: how much non-Clifford and variational expressivity can be removed from the sampling circuit before SQD accuracy degrades? We answer through two complementary compression techniques: gradient-based operator pruning, which discards low-impact excitation operators, and Clifford rounding, which snaps remaining parameters to the nearest Clifford angle. Both of these techniques can be applied to a VQE ansatz on a qubit-reduced Hamiltonian. A systematic ablation study across 21 molecules shows that median SQD error stays within chemical accuracy even at 50\% compression on both axes, while simulation speedup reaches $33\times$. Hardware validation on 6 molecules on IBM quantum hardware confirms up to $2.8\times$ transpiled-depth reduction with zero loss in SQD accuracy. Our implementation can be found at: https://github.com/zkysfls/cs-vqe-sqd
CommentsAccepted by IEEE/ACM 2026 International Conference on Computer-Aided Design (IEEE/ACM ICCAD 2026)
