Bayes or Heisenberg: Who(se) Rules?
This work addresses the challenge of integrating quantum mechanics with neural models for researchers in quantum computing and cognitive science, though it appears incremental as it builds on existing frameworks.
The paper tackles the problem of describing quantum measurement processes by reformulating them as probabilistic equations and approximating these with the Tensor Brain model, a biologically inspired neural network framework for perception and memory.
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.