Biological agents performing approximate Bayesian inference face a strict metabolic trade-off: minimizing variational free energy requires exploring large hypothesis spaces, yet the energetic cost of classical neuronal signaling (action potentials and global broadcast communication) makes exhaustive search infeasible. We propose Quantum-Resonant Netting (QRN), a dual-stage selection architecture in which a low-cost “wave layer” performs transient pre-selection over a hypothesis graph before an irreversible, spike-based fixation/readout step. Formally, we model the wave layer as open wave dynamics on a graph governed by a Gorini–Kossakowski–Sudarshan–Lindblad (GKSL) generator with coherent transport (γ), local dephasing noise (κ), and irreversible capture into a sink/readout state (η), with a diagonal potential V̂ encoding a local prediction-error (free-energy proxy). To make the “optimal noise” claim horizon-independent, we compute the Liouvillian spectral gap g(γ,κ), which controls the asymptotic relaxation time τrelax ≈ 1/g. The ridge of maximal g closely tracks the ridge of maximal finite-horizon success probability Psuccess(T), and a simple T→∞ consistency check shows convergence of argmaxκ Psuccess(T) → argmaxκ g(γ,κ). Keywords: Free Energy Principle; active inference; neuroenergetics; information thermodynamics; open quantum systems; Lindblad dynamics; Environment-Assisted Quantum Transport (ENAQT); Liouvillian spectral gap; cognitome; Neural Darwinism; Signal-to-Noise Ratio.

This paper introduces Quantum-Resonant Netting (QRN), a theoretical framework describing how biological neural networks might utilize transient wave dynamics to solve the "thermodynamic cost of computation" problem. Grounded in the Free Energy Principle and Active Inference, the hypothesis suggests that the brain reduces the metabolic cost of decision-making by implementing a two-tier architecture: Wave Pre-selection ($H_{wave}$): A fast, quasi-reversible evolution of competing hypotheses on a graph-based substrate ("cognitome"), modeled via the GKSL/Lindblad master equation. This stage utilizes environment-assisted transport (ENAQT) to filter hypothesis space before expensive fixation. Spike-based Fixation: A slower, metabolically costly irreversible readout that broadcasts the selected hypothesis. We argue that moderate dephasing acts as a constructive resource, suppressing destructive interference and accelerating relaxation toward the target state (the free-energy minimum) significantly faster than classical diffusion, provided the system operates within a specific "resonant window."