一个量子比特可胜过一个比特：后训练量化的量子优势
One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization
- Fujitsu Limited（富士通公司）
- RIKEN（理化学研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出量子随机存取量化（QRAQ）框架，证明其在重构风险等方面优于共享符号的1比特后训练量化，展现了后训练量化中的量子优势。
AI中文摘要：
1比特后训练量化用仅权重的符号表示每个权重，要求所有部署场景共享同一二值权重矩阵，即便其激活统计量偏好不同的符号模式。我们研究这种共享符号约束，提出量子随机存取量化（QRAQ）框架，该框架将场景相关符号编码到量子随机存取码中，并通过场景匹配的泡利测量检索符号。在显式新鲜副本逻辑读出模型下，QRAQ生成无偏的场景特定二值替代，具有可处理的散粒噪声惩罚。我们证明其与带逐行符号缩放的共享符号1比特后训练量化（PTQ）存在逐行分离；当最优场景相关符号不兼容时，QRAQ实现严格更低的理想重构风险，还推导了保留该分离的有限散粒数与校准噪声条件。固定读出的量子方案可经典模拟，故该模型中相关资源是测量不相容性而非仅量化。最后，我们刻画缩放粒度的作用，提供有限样本保证，并在模拟器实验中评估理想、有限散粒数、噪声及多场景 regime 的表现。
英文摘要：
One-bit post-training quantization represents each weight using only its sign, requiring all deployment contexts to share the same binary weight matrix even when their activation statistics favor different sign patterns. We study this shared-sign constraint and introduce Quantum Random Access Quantization (QRAQ). This framework encodes context-dependent signs in a quantum random-access code and retrieves them via context-matched Pauli measurements. Under an explicit fresh-copy logical readout model, QRAQ produces an unbiased, context-specific binary surrogate with a tractable shot-noise penalty. We prove a row-wise separation from shared-sign one-bit PTQ with signed per-row scales. When the optimal context-wise signs are incompatible, QRAQ achieves a strictly lower ideal reconstruction risk. We also derive finite-shot and calibrated-noise conditions under which this separation is retained. Fixed-readout quantum schemes are classically simulable, so the relevant resource in this model is measurement incompatibility rather than quantization alone. Finally, we characterize the role of scale granularity, provide finite-sample certificates, and evaluate the predicted ideal, finite-shot, noisy, and multi-context regimes in simulator experiments.
