Analytically solvable QRL models reduce expected-return computation from O(e^N) to O(N^I) via path equivalence and transition sparsity, while exhibiting unique optima governed by Zeno effect or discrete/plateau degeneracy at critical energies.
Wendin , Quantum information processing with superconducting circuits: a review , Reports on Progress in Physics, 80 (2017), p
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Complexity scaling and optimal policy degeneracy in quantum reinforcement learning via analytically solvable unitary-control-then-measure models
Analytically solvable QRL models reduce expected-return computation from O(e^N) to O(N^I) via path equivalence and transition sparsity, while exhibiting unique optima governed by Zeno effect or discrete/plateau degeneracy at critical energies.