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[2602.17213] Extending quantum theory with AI-assisted deterministic game theory
Summary
This paper presents an AI-assisted framework for predicting outcomes of complex quantum experiments by integrating deterministic game theory, aiming to extend quantum theory through a local hidden-variable approach.
Why It Matters
The research addresses fundamental questions in quantum mechanics by proposing a novel framework that combines artificial intelligence and game theory. This could lead to new insights into quantum phenomena and the nature of reality, making it relevant for both theoretical physics and AI applications.
Key Takeaways
- Introduces an AI-assisted framework for predicting quantum experiment outcomes.
- Replaces traditional assumptions of free choice with a compatibilistic version.
- Uses neural networks to learn reward functions in a game-theoretic context.
- Demonstrates a proof-of-concept with the EPR 2-2-2 experiment.
- Suggests potential pathways for developing a local hidden-variable theory.
Quantum Physics arXiv:2602.17213 (quant-ph) [Submitted on 19 Feb 2026] Title:Extending quantum theory with AI-assisted deterministic game theory Authors:Florian Pauschitz, Ben Moseley, Ghislain Fourny View a PDF of the paper titled Extending quantum theory with AI-assisted deterministic game theory, by Florian Pauschitz and 2 other authors View PDF HTML (experimental) 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 assumptions (free choice) and assumes Perfect Prediction instead, were described in previous work. In this new research, we learn the reward functions of the game, which contain a hidden variable, using neural networks. The c...
