发表机构
SUNY at Buffalo（纽约州立大学布法罗分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用云光子量子处理器检验离散二进制序列模型，通过Fock态测量证明量子信息容量下限超过100，为量子基础研究提供定量实验手段。
AI 中文摘要
我们的核心前提是，量子力学可能是一个更基本的离散理论的统计极限：任何这样的理论都为物理系统配备有限的信息容量，其与量子统计的偏差由系统使用该容量的多少来控制。我们表明，商用云光子量子处理器已达到足以从下界约束此容量的精度，使用Powers等人的二进制序列模型作为具体测试理论：结果概率源于对长度为$n$的离散序列的计数，量子力学在$n \to \infty$时恢复，而$n$度量了制备态背后寄存器的信息容量。光子Fock态$|1\rangle$、$|1,1\rangle$、 heralded $|2\rangle$以及级联分束器对在可编程干涉仪上测量，主要系统误差在原位确定。模型的组合一致性参数化（通过要求旋转组合而单独选出）以$1.24/n$的偏差恢复量子力学；由参数自助法校准的随机效应似然分析排除了所有$n \le 100$：承载双光子态的寄存器的信息容量，如果有限，则超过$10^2$。级联分束器直接检验组合定律：数据是分割不变的，以$8\sigma$排除朴素计数组合，并确认干涉符号规则。模型无关地，大于$2.3\times10^{-2}$的曲线平均偏差在95%置信水平被排除，且原始发表的线性参数化被彻底排除。由于编译偏移量按电路冻结，它是可校准的，将$10^{-3}$下限（$n \sim 10^3$）开放给当前硬件：云光子处理器是量子基础研究的定量仪器，信息容量是实验上可约束的量。
英文摘要
Our central premise is that quantum mechanics may be the statistical limit of a more fundamental discrete theory: any such theory equips a physical system with a finite information capacity, and its departure from quantum statistics is controlled by how much of that capacity the system uses. We show that commercial cloud photonic quantum processors have reached the precision required to bound this capacity from below, using the binary-sequence model of Powers et al. as the concrete test theory: outcome probabilities arise from counting discrete sequences of length $n$, quantum mechanics is recovered as $n \to \infty$, and $n$ measures the information capacity of the register behind a prepared state. Photon Fock states $|1\rangle$, $|1,1\rangle$, heralded $|2\rangle$, and cascaded beam-splitter pairs are measured on programmable interferometers with dominant systematics determined in situ. The model's composition-consistent parametrization, singled out by requiring that rotations compose, recovers quantum mechanics with deviations $1.24/n$; a random-effects likelihood analysis calibrated by parametric bootstrap excludes all $n \le 100$: the information capacity of the register carrying the two-photon state, if finite, exceeds $10^2$. Cascaded beam splitters test the composition law directly: the data are split-invariant, excluding naive count composition at $8σ$ and confirming the interference-sign rule. Model-independently, curve-averaged deviations from the quantum partition law larger than $2.3\times10^{-2}$ are excluded at 95% CL, and the originally published linear parametrization is excluded outright. Because the compilation offset is frozen per circuit it is calibratable, opening the $10^{-3}$ floor ($n \sim 10^3$) to current hardware: cloud photonic processors are quantitative instruments for quantum foundations, and information capacity an experimentally boundable quantity.
Comments10 pages, 10 figures
