Quantum-Inspired Hamiltonian Optimization, Stochastic Tensor Networks and Adaptive Congestion Routing for Large-Scale QKD Networks

Abstract

Quantum Key Distribution (QKD) networks require routing methodologies capable of jointly optimizing latency, secret key generation rate, congestion, finite capacity and operational security constraints under dynamically evolving traffic conditions. In this work we introduce a quantum-inspired optimization framework for adaptive multi-demand routing in QKD communication networks based on effective Hamiltonian modelling, Quantum Monte Carlo inspired annealing and stochastic Tensor-Network State (TNS) compression. The communication network is represented as a stochastic interacting graph whose routing configurations evolve under an effective Hamiltonian containing latency, keyrate, congestion, risk and capacity terms. The resulting optimization landscape is explored through two complementary approaches: a stochastic Metropolis annealer based on incremental local Hamiltonian updates, and a stochastic boundary-MPS tensor-network approximation that compresses the low-energy routing sector through thermal branch selection. The resulting framework establishes a scalable bridge between QKD network orchestration, statistical-physics-inspired optimization, tensor-network compression and future quantum-native routing systems.