Quantum
TARX Quantum API
Quantum-class optimization via REST. No quantum hardware required. OpenAI-compatible interface.
TARX Quantum runs on your local machine or IBM's 127-qubit Eagle hardware. Same API, same code, different substrate.
What is TARX Quantum?
TARX Quantum exposes five quantum-inspired algorithms through a single REST endpoint. Every solver runs locally by default using classical simulation. When you need real quantum hardware, change one parameter — substrate: "ibm-eagle" — and the same request executes on IBM's 127-qubit Eagle processor. No SDK changes, no circuit design, no Ph.D. required.
Three compute tiers
from openai import OpenAI
# ── Local: quantum-inspired simulation on your machine ──
client = OpenAI(
base_url="http://localhost:11435/v1",
api_key="none"
)
# ── Mesh: distributed quantum simulation across the SuperComputer network ──
client = OpenAI(
base_url="https://api.tarx.com/v1",
api_key="tarx_..."
)
# ── Enterprise: air-gapped fleet with optional IBM Eagle backend ──
client = OpenAI(
base_url="https://tarx.acme-corp.com/v1",
api_key="tarx_..."
)Supported solvers
| Solver | Name | Use Case |
|---|---|---|
qaoa | TARX Optimizer | Combinatorial optimization, routing, scheduling |
grover | TARX Search | Unstructured search, pattern detection, anomaly finding |
qkmeans | TARX Cluster | Unsupervised segmentation, customer analytics |
qsvm | TARX Classifier | Non-linear classification, fraud detection |
qubo | TARX Binary | Binary assignment, portfolio allocation, staffing |
Quick example — route optimization
import requests
response = requests.post("http://localhost:11435/api/solve", json={
"solver": "qaoa",
"problem": {
"type": "route_optimization",
"nodes": [
{"id": "depot", "lat": 40.7128, "lng": -74.0060},
{"id": "a", "lat": 40.7580, "lng": -73.9855},
{"id": "b", "lat": 40.7484, "lng": -73.9857},
{"id": "c", "lat": 40.7614, "lng": -73.9776}
],
"constraints": {"max_distance_km": 50, "vehicle_count": 1}
},
"substrate": "local",
"shots": 1024
})
result = response.json()
print(result["solution"]["route"])
# → ["depot", "c", "a", "b", "depot"]
print(result["solution"]["cost"])
# → 12.4 (km)