用量子编码解决量化模型的符号冲突问题，实现超越经典极限的性能。
One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization
- 将上下文相关的权重符号编码为量子态，通过测量动态读取
- 在理想情况下，重建误差低于传统共享符号方案，且有严格理论保证
- 适合对模型压缩与推理效率要求高的边缘计算场景
一比特后训练量化将每个权重仅用符号表示，要求所有部署场景共享同一组二值权重矩阵，即使激活统计特性不同。本文研究这一共享符号约束，提出量子随机访问量化（QRAQ）框架：将依赖上下文的符号编码为量子随机访问码，并通过匹配上下文的泡利测量读取。在显式新副本逻辑读出模型下，QRAQ生成无偏、上下文特异的二值代理，具有可解析的采样噪声代价。我们证明了行级分离性，当最优符号模式不兼容时，QRAQ的理论重构风险严格更低。进一步推导出有限采样和校准噪声条件下该分离仍成立的条件。固定读出的量子方案可被经典模拟，因此关键资源是测量不可交换性而非量化本身。最后，我们分析了尺度粒度的作用，提供有限样本验证，并在模拟实验中评估了理想、有限采样、噪声及多上下文情形下的表现。
原文摘要 · Abstract (English)
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.
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