Information-Theoretic Constraints on Variational Quantum Optimization: Efficiency Transitions and the Dynamical Lie Algebra

TL;DR

This paper introduces an information-theoretic perspective on the limitations of variational quantum algorithms, linking efficiency transitions to the dynamical Lie algebra structure and information flow stability.

Contribution

It models the optimizer as a Maxwell's Demon, deriving a thermodynamic relation that explains efficiency limits and phase transitions in quantum trainability based on algebraic and information-theoretic properties.

Findings

Polynomial DLA systems exhibit sustained information flow ('Superconductivity').

Exponential DLA systems experience efficiency collapse due to information scrambling.

Quantum trainability is characterized as a thermodynamic phase transition.

Abstract

Variational quantum algorithms are leading candidates for near-term advantage, yet their scalability is fundamentally limited by the ``Barren Plateau'' phenomenon. While traditionally attributed to geometric concentration of measure, I propose an information-theoretic origin: a bandwidth bottleneck in the optimization feedback loop. By modeling the optimizer as a coherent Maxwell's Demon, I derive a thermodynamic constitutive relation, $\Delta E \leq \eta I(S:A)$, where work extraction is strictly bounded by the mutual information established via entanglement. I demonstrate that systems with polynomial Dynamical Lie Algebra (DLA) dimension exhibit ``Information Superconductivity'' (sustained $\eta > 0$), whereas systems with exponential DLA dimension undergo an efficiency collapse when the rate of information scrambling exceeds the ancilla's channel capacity. These results reframe quantum trainability as a thermodynamic phase transition governed by the stability of information flow.