| name | hamiltonian-qkd-routing |
| description | Quantum-inspired Hamiltonian optimization for QKD network routing using effective Hamiltonian modeling, Quantum Monte Carlo annealing, and stochastic Tensor Network State compression. Activation: QKD routing, Hamiltonian optimization, tensor networks, quantum annealing, network orchestration. |
Hamiltonian-Based QKD Network Routing Optimization
Quantum-inspired optimization framework for adaptive multi-demand routing in Quantum Key Distribution (QKD) networks from arXiv:2605.27425 (May 2026). Combines effective Hamiltonian modeling, Quantum Monte Carlo annealing, and stochastic Tensor Network State (TNS) compression.
Core Methodology
Problem
QKD networks require routing that jointly optimizes latency, secret key generation rate, congestion, finite capacity, and operational security constraints under dynamic traffic conditions.
Key Insight
Represent the communication network as a stochastic interacting graph whose routing configurations evolve under an effective Hamiltonian containing latency, keyrate, congestion, risk, and capacity terms.
Two Complementary Approaches
- Stochastic Metropolis Annealer: Incremental local Hamiltonian updates explore the optimization landscape
- Stochastic Boundary-MPS Tensor Network: Compresses low-energy routing sector through thermal branch selection
Numbered Steps
- Model network as stochastic graph: Nodes = network devices, edges = quantum channels with keyrate capacity
- Define effective Hamiltonian: H = α·latency + β·keyrate + γ·congestion + δ·risk + ε·capacity
- Initialize routing configuration: Random or heuristic starting assignment of traffic flows
- Metropolis annealing: Propose local changes, accept/reject based on energy difference ΔH and temperature schedule
- TNS compression (parallel): Represent low-energy routing sector as boundary-MPS tensor network
- Thermal branch selection: Select branches with highest thermal probability for routing decisions
- Converge and deploy: Extract optimal routing configuration from lowest-energy state
Pitfalls
- Temperature schedule: Too fast → trapped in local minimum. Too slow → impractical for real-time routing
- Hamiltonian weights (α, β, γ, δ, ε): Must be calibrated to network-specific priorities
- TNS bond dimension: Too small → loses routing configurations. Too large → computational bottleneck
- Dynamic traffic: Re-optimize when traffic patterns shift significantly; stale solutions become suboptimal
Applications
- Large-scale QKD network orchestration
- Statistical-physics-inspired network optimization
- Tensor-network compression for routing problems
- Future quantum-native routing systems
Verification
- Framework establishes scalable bridge between QKD orchestration, statistical-physics optimization, tensor-network compression, and quantum-native routing
- Validates on dynamic traffic conditions with multiple simultaneous demands
