config.json

{
  "model_type": "reinforcement_learning",
  "task": "quantum_circuit_synthesis",
  "problem_type": "permutation_synthesis",
  "framework": "qiskit-gym",  
  "variants": [
    {"name": "permutation_8qL", "qubits": 8, "topology": "L"},
    {"name": "permutation_12qO", "qubits": 12, "topology": "O"},
    {"name": "permutation_27q", "qubits": 27, "topology": "HEX"},
    {"name": "permutation_33q", "qubits": 33, "topology": "HEX"},
    {"name": "permutation_65q", "qubits": 65, "topology": "HEX"}
  ],
  "license": "apache-2.0",
  "tags": ["quantum-computing", "reinforcement-learning", "circuit-synthesis", "qiskit", "quantum-transpilation", "permutation-synthesis"],
  "paper": {
    "title": "Practical and efficient quantum circuit synthesis and transpiling with reinforcement learning",
    "authors": ["Kremer, D.", "Villar, V.", "Paik, H.", "Duran, I.", "Faro, I.", "Cruz-Benito, J."],
    "arxiv": "2405.13196",
    "year": 2024
  }
}