We present the Random Permutation Sorting System (RPSS), a novel framework for true uniform randomness generation grounded in statistical quantum mechanics. RPSS is built on a pair of conjugate observables, the permutation count and the elapsed sorting time, whose heavy-tailed raw distributions synchronously converge to uniformity through modular reduction. This mathematically proven convergence establishes RPSS as a True Uniform Random Number Generator (TURNG). A practical implementation, QPP-RNG, demonstrates how intrinsic system jitter, arising from microarchitectural noise, memory latency, and scheduling dynamics, interacts with combinatorial complexity to yield a compact, self-stabilizing entropy source. Empirical validation under the NIST SP 800-90B framework confirms rapid entropy convergence and statistically uniform outputs. RPSS thus defines a new class of quantum-inspired entropy engines, where randomness is simultaneously harvested from unpredictable system jitter and amplified by combinatorial processes, offering a robust, platform-independent alternative to conventional entropy sources.

翻译：本文提出随机置换排序系统（RPSS），这是一种基于统计量子力学的新型真均匀随机性生成框架。RPSS建立在一对共轭可观测量——置换计数与已用排序时间——之上，其重尾原始分布通过模约减同步收敛至均匀分布。这种经过数学证明的收敛性确立了RPSS作为真均匀随机数生成器（TURNG）的理论基础。其实用实现QPP-RNG展示了由微架构噪声、内存延迟和调度动态产生的系统固有抖动，如何与组合复杂性相互作用，从而形成一个紧凑的自稳定熵源。在NIST SP 800-90B框架下的实证验证证实了快速的熵收敛与统计均匀的输出。因此，RPSS定义了一类新型的量子启发熵引擎，其随机性同时来源于不可预测的系统抖动并通过组合过程放大，为传统熵源提供了一种鲁棒且平台无关的替代方案。