Extending quantum theory with AI-assisted deterministic game theory
Florian Pauschitz, Ben Moseley, Ghislain Fourny

TL;DR
This paper introduces an AI-assisted framework that models complex quantum experiments as a strategic game, aiming to develop a local hidden-variable theory extending quantum mechanics by learning reward functions with neural networks.
Contribution
It proposes a novel AI-based approach to predict quantum experiment outcomes and learn hidden variables, challenging traditional assumptions like free choice in quantum theory.
Findings
Demonstrated the framework on EPR experiments as a proof-of-concept.
Developed a neural network method to learn reward functions containing hidden variables.
Provided a foundation for discovering a complete local hidden-variable quantum theory.
Abstract
We present an AI-assisted framework for predicting individual runs of complex quantum experiments, including contextuality and causality (adaptive measurements), within our long-term programme of discovering a local hidden-variable theory that extends quantum theory. In order to circumvent impossibility theorems, we replace the assumption of free choice (measurement independence and parameter independence) with a weaker, compatibilistic version called contingent free choice. Our framework is based on interpreting complex quantum experiments as a Chess-like game between observers and the universe, which is seen as an economic agent minimizing action. The game structures corresponding to generic experiments such as fixed-causal-order process matrices or causal contextuality scenarios, together with a deterministic non-Nashian resolution algorithm that abandons unilateral deviation…
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Taxonomy
TopicsQuantum Mechanics and Applications · Advanced Thermodynamics and Statistical Mechanics · Quantum many-body systems
