Quantum

Enterprise Use Cases

TARX Quantum is deployed across four primary verticals. Each section below includes a production-ready code example, deployment notes, and the solver configuration used by real customers.

Defense & Intelligence

Military logistics, ISR route planning, and signals intelligence pattern matching. All TARX Quantum workloads run air-gapped — no data leaves the installation boundary. The local substrate operates without network access after initial install.

QAOA — Multi-vehicle route optimization

import requests

# Optimize ISR patrol routes across 12 waypoints with 3 vehicles
response = requests.post("http://localhost:11435/api/solve", json={
    "solver": "qaoa",
    "problem": {
        "type": "route_optimization",
        "nodes": [
            {"id": "base", "lat": 33.9425, "lng": -118.4081},
            {"id": "wp_alpha", "lat": 34.0195, "lng": -118.4912},
            {"id": "wp_bravo", "lat": 34.0522, "lng": -118.2437},
            {"id": "wp_charlie", "lat": 33.9850, "lng": -118.4695},
            {"id": "wp_delta", "lat": 34.0689, "lng": -118.4452},
            {"id": "wp_echo", "lat": 33.9700, "lng": -118.3900}
        ],
        "constraints": {
            "max_distance_km": 120,
            "vehicle_count": 3,
            "priority_nodes": ["wp_alpha", "wp_bravo"],
            "return_to_base": true
        }
    },
    "substrate": "local",
    "shots": 4096
})

for route in response.json()["solution"]["routes"]:
    print(f'Vehicle {route["vehicle"]}: {" → ".join(route["stops"])}')

Financial Services

Portfolio optimization, fraud detection, and risk scoring. TARX Quantum combines QUBO for allocation decisions with QSVM for real-time transaction classification. Typical deployment: on-premise fleet with IBM Eagle substrate for overnight batch portfolio rebalancing.

QUBO — Portfolio allocation

response = requests.post("http://localhost:11435/api/solve", json={
    "solver": "qubo",
    "problem": {
        "type": "portfolio_allocation",
        "assets": [
            {"id": "SPY", "expected_return": 0.10, "risk": 0.16, "sector": "index"},
            {"id": "TLT", "expected_return": 0.04, "risk": 0.08, "sector": "bonds"},
            {"id": "NVDA", "expected_return": 0.30, "risk": 0.45, "sector": "tech"},
            {"id": "XLE", "expected_return": 0.08, "risk": 0.22, "sector": "energy"},
            {"id": "GLD", "expected_return": 0.06, "risk": 0.12, "sector": "commodity"},
            {"id": "JPM", "expected_return": 0.11, "risk": 0.20, "sector": "financials"}
        ],
        "constraints": {
            "max_assets": 4,
            "max_risk": 0.18,
            "min_return": 0.08,
            "sector_diversification": true,
            "max_per_sector": 1
        }
    },
    "substrate": "local",
    "shots": 8192
})

alloc = response.json()["solution"]
print("Selected:", alloc["selected"])
print(f'Return: {alloc["expected_return"]:.1%}  Risk: {alloc["risk"]:.1%}')

QSVM — Real-time fraud detection

response = requests.post("http://localhost:11435/api/solve", json={
    "solver": "qsvm",
    "problem": {
        "type": "classification",
        "training_data": [
            {"features": [0.12, 0.91, 520, 1, 3], "label": "fraud"},
            {"features": [0.85, 0.10, 45, 0, 1], "label": "legitimate"},
            {"features": [0.09, 0.95, 890, 1, 5], "label": "fraud"},
            {"features": [0.78, 0.22, 120, 0, 1], "label": "legitimate"}
        ],
        "predict": [
            {"features": [0.11, 0.88, 610, 1, 4]},
            {"features": [0.80, 0.15, 55, 0, 1]}
        ]
    },
    "substrate": "local"
})

for p in response.json()["solution"]["predictions"]:
    flag = "🚨 BLOCK" if p["label"] == "fraud" else "✓ ALLOW"
    print(f'{flag}  confidence={p["confidence"]:.0%}')

Cybersecurity

Quantum random number generation for cryptographic entropy, and Grover-accelerated pattern detection for threat hunting across network logs. TARX Quantum replaces expensive SIEM correlation rules with a single API call.

Grover — Threat pattern detection

response = requests.post("http://localhost:11435/api/solve", json={
    "solver": "grover",
    "problem": {
        "type": "pattern_search",
        "dataset": "network_flows_24h",
        "target": {
            "pattern": "c2_beacon",
            "indicators": [
                "periodic_interval_60s",
                "dns_tunnel",
                "encoded_payload"
            ],
            "min_confidence": 0.90
        },
        "search_space_size": 50_000_000
    },
    "substrate": "local",
    "shots": 8192
})

matches = response.json()["solution"]["matches"]
for m in matches:
    print(f'Flow {m["id"]}  src={m["src_ip"]}  confidence={m["confidence"]:.0%}')

QRNG — Cryptographic entropy

# Generate 256 bits of quantum-grade random entropy
response = requests.post("http://localhost:11435/api/solve", json={
    "solver": "grover",
    "problem": {
        "type": "qrng",
        "bits": 256,
        "format": "hex"
    },
    "substrate": "local"
})

entropy = response.json()["solution"]["entropy"]
print(entropy)
# → "a3f8c1d4e9b2...7f6e5d4c3b2a"  (64 hex chars)

Critical Infrastructure

Power grid balancing, water distribution optimization, and batch scheduling for industrial control systems. TARX Quantum runs on hardened edge devices with no cloud dependency.

QAOA — Power grid load balancing

response = requests.post("http://localhost:11435/api/solve", json={
    "solver": "qaoa",
    "problem": {
        "type": "load_balancing",
        "grid": {
            "zones": [
                {"id": "north", "demand_mw": 450, "capacity_mw": 600},
                {"id": "south", "demand_mw": 380, "capacity_mw": 400},
                {"id": "east", "demand_mw": 520, "capacity_mw": 500},
                {"id": "west", "demand_mw": 290, "capacity_mw": 550}
            ],
            "transmission_lines": [
                {"from": "north", "to": "east", "capacity_mw": 100, "loss_pct": 2.1},
                {"from": "west", "to": "south", "capacity_mw": 80, "loss_pct": 1.8},
                {"from": "north", "to": "west", "capacity_mw": 120, "loss_pct": 1.5}
            ]
        },
        "objective": "minimize_transmission_loss"
    },
    "substrate": "local",
    "shots": 4096
})

solution = response.json()["solution"]
for transfer in solution["transfers"]:
    print(f'{transfer["from"]} → {transfer["to"]}: {transfer["mw"]}MW')

Batch scheduling — overnight jobs

# Schedule 8 maintenance jobs across 3 time windows
response = requests.post("http://localhost:11435/api/solve/batch", json={
    "solver": "qaoa",
    "problems": [
        {
            "type": "scheduling",
            "jobs": [
                {"id": "turbine_inspect", "duration_h": 2, "priority": "high"},
                {"id": "valve_replace", "duration_h": 4, "priority": "critical"},
                {"id": "sensor_calibrate", "duration_h": 1, "priority": "medium"}
            ],
            "windows": [
                {"start": "22:00", "end": "06:00", "crew_count": 2}
            ],
            "constraints": {"no_parallel_critical": true}
        }
    ],
    "substrate": "local",
    "shots": 2048
})

schedule = response.json()["results"][0]["solution"]
for slot in schedule["assignments"]:
    print(f'{slot["job"]} → crew {slot["crew"]} at {slot["start_time"]}')