Entanglement as a Structural Complexity Axis: A PAC-Bayesian View of Generalization in Quantum Policies and Value Functions
arXiv:2607.06230v1 Announce Type: cross Abstract: Parameterized quantum circuits (PQCs) are increasingly used as policies and value functions in quantum reinforcement learning, yet it remains unclear when and why quantum policies generalize. We give a PAC-Bayesian account in which generalization is governed not by the raw number of circuit parameters, but by the effective dimension of the Fisher geometry induced by the circuit. This quantity is inflated by entanglement, making entangling connectivity an independent axis of complexity.In controlled experiments that fix the number of trainable r
The accelerating use of Parameterized Quantum Circuits in quantum reinforcement learning necessitates new theoretical frameworks to understand their generalization capabilities.
This research provides a fundamental understanding of quantum AI generalization, moving beyond simple parameter counts to include entanglement as a key complexity axis.
The way researchers and developers design and evaluate quantum policies and value functions will shift, focusing more on entanglement structures rather than just circuit size.
- · Quantum computing researchers
- · Quantum machine learning developers
- · AI theory
- · Overly simplistic quantum AI models
- · Classical generalization theories
Improved design principles for quantum machine learning algorithms with better generalization properties.
Faster development and deployment of more robust quantum AI applications across various industries.
The acceleration of quantum advantage in practical AI applications due to more effective quantum algorithm design.
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