用神经网络模拟量子测量的概率演化，连接脑科学与量子物理。
Bayes or Heisenberg: Who(se) Rules?
- 将量子测量重构成概率态矢量的动态过程
- 通过张量大脑模型逼近该概率演化行为
- 适合对神经计算与量子机制交叉感兴趣的读者
尽管量子系统通常由量子态矢量描述，我们发现某些情况下其测量过程可重新表述为基于概率态矢量的随机方程。这些概率表示可进一步由张量大脑（Tensor Brain, TB）模型的神经网络动力学近似。张量大脑是近期提出的用于建模大脑感知与记忆的框架，提供了一种生物启发机制，能高效地将生成的符号表示融入推理过程。
原文摘要 · Abstract (English)
Although quantum systems are generally described by quantum state vectors, we show that in certain cases their measurement processes can be reformulated as probabilistic equations expressed in terms of probabilistic state vectors. These probabilistic representations can, in turn, be approximated by the neural network dynamics of the Tensor Brain (TB) model. The Tensor Brain is a recently proposed framework for modeling perception and memory in the brain, providing a biologically inspired mechanism for efficiently integrating generated symbolic representations into reasoning processes.
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