QAOA route optimisation for logistics and supply chains

H = A × Σ_i (1 - Σ_t x[i,t])²          (each city visited exactly once)
  + A × Σ_t (1 - Σ_i x[i,t])²          (exactly one city per time step)
  + B × Σ_t Σ_{i≠j} W[i,j] x[i,t] x[j,t+1]   (minimise total distance)

import os
import time
import json
import requests
import numpy as np

API_KEY = os.environ["QCOS_API_KEY"]
BASE_URL = "https://api.softquantus.com"
HEADERS = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}

# 5-city distance matrix (symmetric)
cities = ["Paris", "Berlin", "Madrid", "Rome", "Warsaw"]
distances = np.array([
    [0,  878, 1270, 1110, 1365],
    [878,  0, 1870,  1185,  520],
    [1270, 1870, 0,  1360, 2570],
    [1110, 1185, 1360,  0,  1320],
    [1365,  520, 2570, 1320,   0]
])

# Submit QAOA TSP job
resp = requests.post(
    f"{BASE_URL}/api/v1/algorithms/qaoa",
    headers=HEADERS,
    json={
        "problem_type": "tsp",
        "problem_data": {
            "distance_matrix": distances.tolist(),
            "city_names": cities
        },
        "backend": "statevector_simulator",
        "shots": 10000,
        "options": {
            "depth": 4,
            "optimizer": "COBYLA",
            "constraint_penalty": 8.0
        }
    }
)
resp.raise_for_status()
job_id = resp.json()["job_id"]
print(f"TSP QAOA job: {job_id}")

# Poll
while True:
    r = requests.get(f"{BASE_URL}/api/v1/jobs/{job_id}", headers=HEADERS).json()
    if r["status"] in ("completed", "failed"):
        break
    time.sleep(5)

result = r["result"]
route = result["optimal_route"]
total_km = result["total_distance"]

print(f"\nOptimal Route:")
for i, city in enumerate(route):
    if i < len(route) - 1:
        d = distances[cities.index(city)][cities.index(route[i+1])]
        print(f"  {city} → {route[i+1]}: {d} km")
print(f"\nTotal distance: {total_km} km")
print(f"Approximation ratio: {result.get('approximation_ratio', 'N/A')}")

{
  "problem_type": "vrp",
  "problem_data": {
    "depots": [{"id": "depot_0", "lat": 48.85, "lon": 2.35}],
    "customers": [
      {"id": "c1", "lat": 48.87, "lon": 2.33, "demand": 3, "time_window": [9, 12]},
      {"id": "c2", "lat": 48.84, "lon": 2.38, "demand": 5, "time_window": [10, 14]}
    ],
    "vehicles": [
      {"id": "v1", "capacity": 15, "depot": "depot_0"},
      {"id": "v2", "capacity": 15, "depot": "depot_0"}
    ]
  },
  "backend": "statevector_simulator",
  "shots": 20000
}

# Submit scheduling optimisation
resp = requests.post(
    f"{BASE_URL}/api/v1/algorithms/qaoa",
    headers=HEADERS,
    json={
        "problem_type": "job_shop",
        "problem_data": {
            "jobs": [
                {"id": "J1", "operations": [{"machine": "M1", "duration": 3}, {"machine": "M2", "duration": 2}]},
                {"id": "J2", "operations": [{"machine": "M2", "duration": 4}, {"machine": "M3", "duration": 1}]},
                {"id": "J3", "operations": [{"machine": "M1", "duration": 2}, {"machine": "M3", "duration": 3}]}
            ],
            "machines": ["M1", "M2", "M3"],
            "objective": "minimize_makespan"
        },
        "backend": "statevector_simulator",
        "shots": 8192
    }
)
resp.raise_for_status()
job_id = resp.json()["job_id"]

curl -X POST https://api.softquantus.com/api/v1/estimate \
  -H "Authorization: Bearer $QCOS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "algorithm": "qaoa",
    "problem_type": "tsp",
    "cities": 10,
    "backend": "aws_ionq_aria_1",
    "shots": 10000
  }'

{
  "estimated_credits": 24.80,
  "estimated_qubits": 100,
  "estimated_time_seconds": 120,
  "circuit_depth": 480,
  "cost_usd_equivalent": 2.48
}