Machine learning for Schrödinger’s cat paradox

Lizhi Xin, Kevin Z. Xin, Houwen Xin

Published Mar 30, 2023• 0 citations

Abstract

Abstract Unlike other quantum models that explain the Schrödinger’s cat paradox through rigorous mathematical structure, we propose a computational model to describe and explain Schrödinger’s cat paradox based on Darwin’s natural selection. Our proposed algorithmic model discovers laws of nature by only learning observed historical data (sequential measurement of observables) based on maximizing the observer’s expected value. There is no Schrödinger differential equation and quantum probability in our model; our model has an emphasis on machine learning, where the observer builds up his/her experience by being rewarded or punished for each decision he/she makes, and eventually leads to discovering the Born rule. We do not model with the usual wave function, but with quantum decision tree that simulates the observer’s decision process, a quantum decision tree can be constructed from eight basic quantum gates as well as three logic operations; each quantum decision tree includes a set of strategies; every time a decision is made, the observer chooses a strategy from the quantum decision tree’s strategy pool first, and then chooses an action (believe whether the cat is alive or dead)) based on the degree of belief which is evolved by genetic programming.

DOI: 10.21203/rs.3.rs-2751586/v1

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