A free energy principle for generic quantum systems
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Abstract (arXiv)
The Free Energy Principle (FEP) states that under suitable conditions of weak coupling, random dynamical systems with sufficient degrees of freedom will behave so as to minimize an upper bound, formalized as a variational free energy, on surprisal (a.k.a., self-information). This upper bound can be read as a Bayesian prediction error. Equivalently, its negative is a lower bound on Bayesian model evidence (a.k.a., marginal likelihood). In short, certain random dynamical systems evince a kind of self-evidencing. Here, we reformulate the FEP in the formal setting of spacetime-background free, scale-free quantum information theory. We show how generic quantum systems can be regarded as observers, which with the standard freedom of choice assumption become agents capable of assigning semantics to observational outcomes. We show how such agents minimize Bayesian prediction error in environments characterized by uncertainty, insufficient learning, and quantum contextuality. We show that in its quantum-theoretic formulation, the FEP is asymptotically equivalent to the Principle of Unitarity. Based on these results, we suggest that biological systems employ quantum coherence as a computational resource and - implicitly - as a communication resource. We summarize a number of problems for future research, particularly involving the resources required for classical communication and for detecting and responding to quantum context switches.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Dec 2021.
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Citing papers per year: 2022: 2; 2023: 2; 2024: 0; 2025: 0; 2026: 1.
5 papers.
- 2022Designing Ecosystems of Intelligence from First Principlescited by 23
- 2023Separability, Contextuality, and the Quantum Frame Problemcited by 1
- 2026Remapping and navigation of an embedding space via error minimization: a fundamental organizational principle of cognition in natural and artificial systemscited by 1
- 2022Deep Neural Networks as the Semi-classical Limit of Topological Quantum Neural Networks: The problem of generalisationcited by 0
- 2023Ensoul: A framework for the creation of self organizing intelligent ultra low power systems (SOULS) through evolutionary enerstatic networkscited by 0
- 2026Remapping and navigation of an embedding space via error minimization: a fundamental organizational principle of cognition in natural and artificial systems20 Jan 2026
- 2023Ensoul: A framework for the creation of self organizing intelligent ultra low power systems (SOULS) through evolutionary enerstatic networks26 Apr 2023
- 2023Separability, Contextuality, and the Quantum Frame Problem19 Apr 2023
- 2022Designing Ecosystems of Intelligence from First Principles2 Dec 2022
- 2022Deep Neural Networks as the Semi-classical Limit of Topological Quantum Neural Networks: The problem of generalisation25 Oct 2022