From Probabilistic Cloud to Python — A Control-Layer Language for Quantum Computing and Hardware Validation

Abstract

The probabilistic cloud state machine (PCSM) theory [1] formalises the classical control layer of quantum computing as a geometric model over the space of stable probability distributions. This paper reports the full implementation path from that theory to a usable Python programming interface, together with real-hardware validation results on the "Origin Wukong" superconducting quantum chip. The six-layer stack is: physical-calibration layer, symbol-table layer, structure layer, semantic-resolution layer, compiler layer, and the pycloud API layer. Software engineers with no quantum-physics background can prepare, transform and measure probabilistic cloud symbols in three lines of Python; decide() provides measurement-feedback branching and composes natively with Python control flow. The real-hardware results show that: (1) In a single real-hardware calibration, the anchoring fidelity of the single- and two-qubit symbols is A=0.889–0.998A=0.889–0.998 (in the JSJS convention; for the complete symbol table see §5.3 and Appendix B of the v3.1 theory paper, where unusable symbols go as low as A=0.22A=0.22); the ceiling for a deterministic symbol is about A≈0.915A≈0.915, set by the readout error, and raising shots cannot improve it (§4.2); (2) parameterised rotation gates show significant anomalies at specific angles (RY(π/4): deviation 0.136 ≈ 8.6σ; RZ(π/2): deviation 0.167 ≈ 7.4σ), while RY(0), RY(π/2) and RY(π) are normal; the discrete gates (H, X, CNOT, S) are normal in this data set (§4.4). We report the anomaly as it is and state explicitly that the present data are insufficient to determine its root cause; (3) a three-qubit GHZ state gave M=3.9541M=3.9541 and M=3.9720M=3.9720 in two measurements, both far above the classical limit 2, confirming the physical realizability of multipartite entanglement (§4.5). The two measurements are technical replicates of the same circuit in the same calibration window, not independent experiments; (4) a recalibration record of 40 intermittent samples (2026-09-05 08:34–15:08, a span of about 6.6 hours, 500 shots per sample) shows that the standard deviation of P(0)P(0) for the ∣+⟩∣+⟩ symbol prepared by an H gate is 2.71 times the pure shot-noise expectation, and its mean deviates from 0.5 by 5.6 standard errors — a real deviation beyond statistical fluctuation, so "calibrate once and reuse indefinitely" is unreliable (§4.3). Note: these are intermittent samples, so no drift rate or drift timescale is claimed. (5, new in v0.5, theoretical result) the theory paper v3.1 gives a two-layer ceiling for the control layer: the first layer is the hardware layer (the readout error determines A≤0.915A≤0.915, §4.2); the second is the coherence blind spot (theorem-level, hardware-independent): ∣+⟩∣+⟩ and ∣−⟩∣−⟩ anchor classically at A=1A=1 in the computational basis and are completely indistinguishable, while their quantum JS distance is at the maximum ln⁡2ln2 — regardless of shots and readout quality, the control layer's distinguishing power for the same-diagonal-pair family is identically zero (§5.6). "Symbols bound to a measurement basis" is thereby promoted from an engineering requirement to a necessity of the projection structure; (6, new in v0.5, proposal) the directionality-monitoring primitives: the third-order cumulant TT and the KL asymmetry difference (the difference of the forward and backward KL divergences, ≈−16T dθ3≈−61Tdθ3) can serve as a directionality probe for the blind spot of second-order statistics (AA), to be used to test the relation between directionality defects and the parameterised-gate anomaly (§5.7, 【not yet run on hardware · proposal】). Keywords: probabilistic cloud state machine; quantum programming language; pycloud; anchoring fidelity; hardware validation; GHZ; Mermin inequality; Origin Wukong; coherence blind spot; directionality monitoring 概率云状态机理论 [1] 将量子计算的经典控制层形式化为稳定概率分布空间上的几何模型。本文报告从该理论到可用 Python 编程接口的实现路径,以及在"本源悟空"超导量子芯片上的真机验证结果。 六层技术栈为:物理标定层、符号表层、结构层、语义解析层、编译器层、pycloud API 层。软件工程师无需量子物理背景,即可用三行 Python 代码完成概率云符号的准备、变换与测量;decide() 提供基于测量反馈的分支决策,并原生兼容 Python 的控制流。 真机结果表明: (1)单比特与两比特符号在单次真机标定中的锚定保真度为 A=0.889–0.998A=0.889–0.998(JSJS 口径;完整符号表另见 §5.3 与理论论文 v3.1 附录 B,其中不可用符号 AA 低至 0.22);确定性符号的上限约 A≈0.915A≈0.915,该上限由读出误差决定,提高 shots 不能改善(§4.2); (2)参数化旋转门在特定角度出现显著异常(RY(π/4) 偏差 0.136 ≈ 8.6σ、RZ(π/2) 偏差 0.167 ≈ 7.4σ),而 RY(0)、RY(π/2)、RY(π) 正常;离散门(H、X、CNOT、S)本次数据正常(§4.4)。本文如实报告该异常、并说明现有数据不足以判定根因; (3)三比特 GHZ 态两次测得 M=3.9541M=3.9541 与 M=3.9720M=3.9720,均远超经典极限 2,确认多体纠缠的物理可实现性(§4.5)。两次测量为同一线路、同一校准窗口内的技术重复,不构成独立实验; (4)**40 个断续采样点(2026-09-05 08:34–15:08,跨度约 6.6 小时,每点 500 shots)**的重标定记录显示:H 门制备的 ∣+⟩∣+⟩ 符号其 P(0)P(0) 的标准差是纯散粒噪声期望的 2.71 倍,均值偏离 0.5 达 5.6 个标准误 —— 存在超出统计涨落的真实偏差,故"一次标定、长期沿用"不可靠(§4.3)。注意:断续采样,不据此主张漂移速率或时间尺度。 (5,v0.5 新增,理论结果) 理论论文 v3.1 给出控制层的两层天花板:第一层是硬件层(读出误差决定 A≤0.915A≤0.915,§4.2);第二层是相干盲区(定理级、硬件无关):∣+⟩∣+⟩ 与 ∣−⟩∣−⟩ 在计算基下经典锚定 A=1A=1 且完全不可区分,而其量子 JS 距离为最大值 ln⁡2ln2——无论 shots 与读出质量如何,控制层对同对角对家族的区分能力恒为零(§5.6)。"符号绑定测量基"由此从工程要求升级为投影结构的必然; (6,v0.5 新增,提案) 方向性监测原语:三阶累积量 TT 与 KL 不对称差(正反两向 KL 之差 ≈−16T dθ3≈−61Tdθ3)可作为对二阶统计(AA)盲区的方向性探针,用于检验方向性缺陷与参数化门异常的关系(§5.7,【未上机·提案】)。 关键词: 概率云状态机;量子编程语言;pycloud;锚定保真度;真机验证;GHZ;Mermin 不等式;本源悟空;相干盲区;方向性监测