NetworkOptimization

Summary of Results — IQM Emerald QAOA Network Optimization

Quantum Hardware: IQM Emerald (54 qubits)
Algorithm: 1-layer QAOA (γ=0.5, β=0.8)
Shots: 4096

Decoded Optimal Solution

• Generation
  • S1 = 4 units
  • S2 = 2 units
• Battery
  • Charge = 2
  • Discharge = 2
  • State-of-Charge = 0 (neutral)
• Flows
  • Major energy routes:
    S1 → D1 = 3, S2 → D1 = 3, S2 → D2 = 3
  • Minor balancing flows via battery nodes

Interpretation

The QAOA circuit found a valid feasible configuration minimizing generation cost while maintaining network flow balance and respecting capacities.
This confirms that a quantum optimization algorithm can encode and solve realistic constrained network problems using current IQM hardware.

How to execute

Create a python virtual environment

python -m venv .venv .venv\Scripts\activate

Install required packages

pip install qiskit iqm-qiskit-provider numpy

Setup your IQM API Token

set IQM_TOKEN=your_token_here

Run

python iqm_network_opt_qiskit_emerald.py