一次一个事件地模拟量子物理实验
Simulating Quantum Physics Experiments One Event at a Time
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中文总结 AI 辅助
本文提出逐事件模拟（EBES）框架，通过因果驱动的确定性学习机以离散事件方式生成数据，解决量子测量悖论，并高效模拟超出标准量子理论范围的实验现象。
中文摘要 AI 辅助
本文探讨了经验实验室数据与连续理论物理模型之间的根本分歧，将“量子测量悖论”框定为一种本体论错误，具体而言是E.T. Jaynes的“思维投射谬误”，即波函数等抽象数学工具被误认为物理实在。尽管量子理论成功地预测了系综平均值，但它本质上对单个、逐事件的探测统计保持沉默。为了在不诉诸波函数坍缩等临时解释的情况下解决这些概念之谜，本文引入了逐事件模拟（EBES）。作为一个模块化的离散事件模拟框架，利用因果驱动的确定性学习机（DLMs），EBES绕过了连续时间和预先指定的概率分布。相反，它以严格局域化的因果方式自下而上地生成离散数据。最终，EBES提供了一种计算高效的方法，反映了实际实验室观测，模拟了完全超出标准量子理论范围的现象。
英文摘要
This text explores the fundamental divide between empirical laboratory data and continuous theoretical physics models, framing the "quantum measurement paradox" as an ontological error, specifically E.T. Jaynes' ``mind projection fallacy'' where abstract mathematical tools like wavefunctions are mistaken for physical reality. While quantum theory successfully predicts ensemble averages, it remains inherently silent on individual, event-by-event detection statistics. To resolve these conceptual mysteries without resorting to ad-hoc explanations like wavefunction collapse, the text introduces Event-By-Event Simulation (EBES). As a modular, discrete-event simulation framework utilizing causally-driven Deterministic Learning Machines (DLMs), EBES bypasses continuous time and pre-specified probability distributions. Instead, it generates discrete data from the bottom up in a strictly localized, cause-and-effect manner. Ultimately, EBES provides a computationally efficient methodology that mirrors actual laboratory observations, modeling phenomena that lie entirely beyond the reach of standard quantum theory.
发表机构
- Jülich Supercomputing Centre, Forschungszentrum Jülich（于利希超级计算中心，于利希研究中心）
机构由 AI 辅助整理，请以论文原文为准。
