# DSF Quantum SDK
Reduce costos y complejidad en evaluación de inferencias cuánticas mediante simulación adaptativa y compilación inteligente.
Ejecuta y valida workloads híbridos 10× más rápido con mínima sobrecarga.
## 🚀 Why DSF Quantum?
Los entornos cuánticos tradicionales requieren acceso costoso a hardware QPU y tiempos de espera prolongados.
DSF Quantum SDK encapsula la lógica de evaluación, simulación y compilación en una capa unificada que permite:
- Simular, evaluar y compilar circuitos cuánticos desde código Python o remoto.
- Reducir pruebas reales en hardware mediante simuladores inteligentes.
- Optimizar pipelines híbridos con inferencias aceleradas en CPU/GPU.
## 📚 Core Concepts
- **Adaptive Simulation** – Ejecuta circuitos cuánticos de forma incremental con reducción adaptativa de ruido.
- **Config-as-topology** – Define tus qubits, compuertas y prioridades en forma declarativa.
- **Quantum Compilation** – Convierte circuitos de alto nivel en representaciones optimizadas (QASM, Tensor, o BinaryGraph).
- **Hybrid Evaluation** – Conecta resultados clásicos y cuánticos dentro de un mismo pipeline.
- **Enterprise**: incluye soporte para "quantum workers" distribuidos y compilación hacia hardware real o simuladores especializados.
## 📦 Installation
```bash
pip install dsf-quantum-sdk
```
Opcionalmente, apunta el SDK hacia tu backend:
```python
import os
from dsf_quantum_sdk import QuantumSDK
sdk = QuantumSDK(
base_url=os.getenv("DSF_QUANTUM_BASE_URL"), # e.g. https://dsf-quantum-api.vercel.app
tier="community"
)
```
## 🎯 Quick Start
### Community
```python
from dsf_quantum_sdk import QuantumSDK
sdk = QuantumSDK() # tier community por defecto
# Crear un circuito básico
circuit = sdk.create_circuit()
circuit.add_qubit('q0')
circuit.add_gate('H', targets=['q0'])
circuit.add_measure('q0')
# Simular localmente
result = sdk.simulate(circuit)
print("Probabilidades:", result['probabilities'])
```
### Professional
```python
from dsf_quantum_sdk import QuantumSDK
sdk = QuantumSDK(license_key="PRO-2026-12-31-XXXX", tier="professional")
circuit = (sdk.create_circuit()
.add_qubit('q0')
.add_qubit('q1')
.add_gate('H', targets=['q0'])
.add_gate('CX', targets=['q0','q1'])
.add_measure('q0')
.add_measure('q1')
)
# Evaluar en batch (hasta 1000 simulaciones)
experiments = [circuit.to_dict() for _ in range(10)]
scores = sdk.batch_simulate(experiments)
print("Resultados batch:", scores)
```
### Enterprise
```python
from dsf_quantum_sdk import QuantumSDK
sdk = QuantumSDK(license_key="ENT-2026-12-31-XXXX", tier="enterprise")
# Compilación + ejecución híbrida
circuit = (sdk.create_circuit()
.add_qubit('q0')
.add_qubit('q1')
.add_gate('H', targets=['q0'])
.add_gate('CX', targets=['q0','q1'])
.add_measure('q0')
.add_measure('q1')
)
compiled = sdk.compile(circuit, target="qasm")
hybrid_result = sdk.hybrid_run(compiled, classical_inputs={"alpha": 0.7})
print("Resultado híbrido:", hybrid_result)
```
## 🧠 Advanced Pipelines
### Quantum Worker Orchestration (Enterprise)
Permite ejecutar simulaciones o evaluaciones distribuidas en workers configurados en la nube (GCP, AWS, Vercel, etc.):
```python
task = sdk.worker_submit(
circuit=circuit,
backend="gcr.io/dsf-quantum-475822/quantum-worker",
batch_size=50
)
print("Tarea enviada:", task["id"])
```
Puedes monitorear progreso:
```python
status = sdk.worker_status(task["id"])
print(status)
```
## 🔧 Fine-Tuned Recipes
### A) Community — Simple Superposition
```python
circuit = (sdk.create_circuit()
.add_qubit('q0')
.add_gate('H', targets=['q0'])
.add_measure('q0')
)
result = sdk.simulate(circuit)
print(result['probabilities'])
```
### B) Professional — Entanglement Test
```python
circuit = (sdk.create_circuit()
.add_qubit('q0').add_qubit('q1')
.add_gate('H', targets=['q0'])
.add_gate('CX', targets=['q0','q1'])
.add_measure('q0').add_measure('q1')
)
sim = sdk.batch_simulate([circuit.to_dict()]*100)
print("Promedio correlación:", sum(x["correlation"] for x in sim)/100)
```
### C) Enterprise — Hybrid Workflow
```python
cfg = {"iterations": 3, "noise_level": 0.01}
hybrid = sdk.hybrid_run(circuit, classical_inputs=cfg)
print("Output:", hybrid)
```
## ⚡ Performance Tips
- Usa `batch_simulate()` en lugar de `simulate()` para grandes volúmenes.
- `compile()` puede cachearse para reutilizar topologías.
- Usa `worker_submit()` para ejecutar tareas en paralelo.
- `hybrid_run()` acepta datos clásicos para reducir overhead cuántico.
## 💡 Use Cases
### 1. Quantum Evaluation
Evalúa múltiples variantes de un circuito para analizar estabilidad y fidelidad:
```python
scores = sdk.batch_simulate([circuit.to_dict() for _ in range(500)])
```
### 2. Hybrid Optimization
Integra valores clásicos y cuánticos:
```python
result = sdk.hybrid_run(circuit, classical_inputs={"alpha": 0.5, "beta": 0.9})
```
### 3. Distributed Compilation
Despliega compilaciones pesadas a workers remotos:
```python
sdk.worker_submit(circuit, backend="gcr.io/dsf-quantum-475822/quantum-worker")
```
## 📊 Rate Limits
| Tier | Simulations/Day | Batch Size | Worker Jobs | Compilation |
|--------------|-----------------|------------|-------------|-------------|
| Community | 500 | ❌ | ❌ | ❌ |
| Professional | ilimitado | ✅ ≤1000 | limitado | ✅ |
| Enterprise | ilimitado | ✅ ≤1000 | ✅ | ✅ (QPU) |
## 🆚 Tier Comparison
| Feature | Community | Professional | Enterprise |
|---------------------|-----------|--------------|------------|
| Local simulation | ✅ | ✅ | ✅ |
| Batch simulation | ❌ | ✅ | ✅ |
| Quantum compilation | ❌ | ✅ | ✅ (QPU+) |
| Hybrid evaluation | ❌ | ✅ | ✅ |
| Distributed workers | ❌ | ❌ | ✅ |
| Cloud orchestration | ❌ | ❌ | ✅ |
## 📖 API Reference
### Initialization
```python
QuantumSDK(
tier='community'|'professional'|'enterprise',
license_key=None,
base_url=None,
timeout=30
)
```
### Core Methods
- `create_circuit()` → Crea una nueva topología.
- `simulate(circuit)` → Simula localmente.
- `batch_simulate(circuits)` → Simula múltiples circuitos.
- `compile(circuit, target="qasm"|"binary"|"tensor")` → Compila el circuito.
- `hybrid_run(circuit, classical_inputs)` → Evalúa circuito con datos clásicos.
- `worker_submit(circuit, backend, batch_size)` → Envío distribuido.
- `worker_status(task_id)` → Monitorea progreso.
## ⚠️ Common Errors
| Código | Causa | Solución |
|------------------------|----------------------------|------------------------------|
| 422 Invalid Circuit | Falta topología o medida | Añade al menos una medida |
| 429 Rate Limit | Excediste el límite diario | Espera o sube de tier |
| 500 Worker Unavailable | El worker no responde | Reintenta o usa otro backend |
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"description": "\ufeff# DSF Quantum SDK\r\n\r\nReduce costos y complejidad en evaluaci\u00f3n de inferencias cu\u00e1nticas mediante simulaci\u00f3n adaptativa y compilaci\u00f3n inteligente.\r\nEjecuta y valida workloads h\u00edbridos 10\u00d7 m\u00e1s r\u00e1pido con m\u00ednima sobrecarga.\r\n\r\n## \ud83d\ude80 Why DSF Quantum?\r\n\r\nLos entornos cu\u00e1nticos tradicionales requieren acceso costoso a hardware QPU y tiempos de espera prolongados.\r\nDSF Quantum SDK encapsula la l\u00f3gica de evaluaci\u00f3n, simulaci\u00f3n y compilaci\u00f3n en una capa unificada que permite:\r\n\r\n- Simular, evaluar y compilar circuitos cu\u00e1nticos desde c\u00f3digo Python o remoto.\r\n- Reducir pruebas reales en hardware mediante simuladores inteligentes.\r\n- Optimizar pipelines h\u00edbridos con inferencias aceleradas en CPU/GPU.\r\n\r\n## \ud83d\udcda Core Concepts\r\n\r\n- **Adaptive Simulation** \u2013 Ejecuta circuitos cu\u00e1nticos de forma incremental con reducci\u00f3n adaptativa de ruido.\r\n- **Config-as-topology** \u2013 Define tus qubits, compuertas y prioridades en forma declarativa.\r\n- **Quantum Compilation** \u2013 Convierte circuitos de alto nivel en representaciones optimizadas (QASM, Tensor, o BinaryGraph).\r\n- **Hybrid Evaluation** \u2013 Conecta resultados cl\u00e1sicos y cu\u00e1nticos dentro de un mismo pipeline.\r\n- **Enterprise**: incluye soporte para \"quantum workers\" distribuidos y compilaci\u00f3n hacia hardware real o simuladores especializados.\r\n\r\n## \ud83d\udce6 Installation\r\n\r\n```bash\r\npip install dsf-quantum-sdk\r\n```\r\n\r\nOpcionalmente, apunta el SDK hacia tu backend:\r\n\r\n```python\r\nimport os\r\nfrom dsf_quantum_sdk import QuantumSDK\r\n\r\nsdk = QuantumSDK(\r\n base_url=os.getenv(\"DSF_QUANTUM_BASE_URL\"), # e.g. https://dsf-quantum-api.vercel.app\r\n tier=\"community\"\r\n)\r\n```\r\n\r\n## \ud83c\udfaf Quick Start\r\n\r\n### Community\r\n\r\n```python\r\nfrom dsf_quantum_sdk import QuantumSDK\r\n\r\nsdk = QuantumSDK() # tier community por defecto\r\n\r\n# Crear un circuito b\u00e1sico\r\ncircuit = sdk.create_circuit()\r\ncircuit.add_qubit('q0')\r\ncircuit.add_gate('H', targets=['q0'])\r\ncircuit.add_measure('q0')\r\n\r\n# Simular localmente\r\nresult = sdk.simulate(circuit)\r\nprint(\"Probabilidades:\", result['probabilities'])\r\n```\r\n\r\n### Professional\r\n\r\n```python\r\nfrom dsf_quantum_sdk import QuantumSDK\r\n\r\nsdk = QuantumSDK(license_key=\"PRO-2026-12-31-XXXX\", tier=\"professional\")\r\n\r\ncircuit = (sdk.create_circuit()\r\n .add_qubit('q0')\r\n .add_qubit('q1')\r\n .add_gate('H', targets=['q0'])\r\n .add_gate('CX', targets=['q0','q1'])\r\n .add_measure('q0')\r\n .add_measure('q1')\r\n)\r\n\r\n# Evaluar en batch (hasta 1000 simulaciones)\r\nexperiments = [circuit.to_dict() for _ in range(10)]\r\nscores = sdk.batch_simulate(experiments)\r\nprint(\"Resultados batch:\", scores)\r\n```\r\n\r\n### Enterprise\r\n\r\n```python\r\nfrom dsf_quantum_sdk import QuantumSDK\r\n\r\nsdk = QuantumSDK(license_key=\"ENT-2026-12-31-XXXX\", tier=\"enterprise\")\r\n\r\n# Compilaci\u00f3n + ejecuci\u00f3n h\u00edbrida\r\ncircuit = (sdk.create_circuit()\r\n .add_qubit('q0')\r\n .add_qubit('q1')\r\n .add_gate('H', targets=['q0'])\r\n .add_gate('CX', targets=['q0','q1'])\r\n .add_measure('q0')\r\n .add_measure('q1')\r\n)\r\n\r\ncompiled = sdk.compile(circuit, target=\"qasm\")\r\nhybrid_result = sdk.hybrid_run(compiled, classical_inputs={\"alpha\": 0.7})\r\nprint(\"Resultado h\u00edbrido:\", hybrid_result)\r\n```\r\n\r\n## \ud83e\udde0 Advanced Pipelines\r\n\r\n### Quantum Worker Orchestration (Enterprise)\r\n\r\nPermite ejecutar simulaciones o evaluaciones distribuidas en workers configurados en la nube (GCP, AWS, Vercel, etc.):\r\n\r\n```python\r\ntask = sdk.worker_submit(\r\n circuit=circuit,\r\n backend=\"gcr.io/dsf-quantum-475822/quantum-worker\",\r\n batch_size=50\r\n)\r\nprint(\"Tarea enviada:\", task[\"id\"])\r\n```\r\n\r\nPuedes monitorear progreso:\r\n\r\n```python\r\nstatus = sdk.worker_status(task[\"id\"])\r\nprint(status)\r\n```\r\n\r\n## \ud83d\udd27 Fine-Tuned Recipes\r\n\r\n### A) Community \u2014 Simple Superposition\r\n\r\n```python\r\ncircuit = (sdk.create_circuit()\r\n .add_qubit('q0')\r\n .add_gate('H', targets=['q0'])\r\n .add_measure('q0')\r\n)\r\nresult = sdk.simulate(circuit)\r\nprint(result['probabilities'])\r\n```\r\n\r\n### B) Professional \u2014 Entanglement Test\r\n\r\n```python\r\ncircuit = (sdk.create_circuit()\r\n .add_qubit('q0').add_qubit('q1')\r\n .add_gate('H', targets=['q0'])\r\n .add_gate('CX', targets=['q0','q1'])\r\n .add_measure('q0').add_measure('q1')\r\n)\r\nsim = sdk.batch_simulate([circuit.to_dict()]*100)\r\nprint(\"Promedio correlaci\u00f3n:\", sum(x[\"correlation\"] for x in sim)/100)\r\n```\r\n\r\n### C) Enterprise \u2014 Hybrid Workflow\r\n\r\n```python\r\ncfg = {\"iterations\": 3, \"noise_level\": 0.01}\r\nhybrid = sdk.hybrid_run(circuit, classical_inputs=cfg)\r\nprint(\"Output:\", hybrid)\r\n```\r\n\r\n## \u26a1 Performance Tips\r\n\r\n- Usa `batch_simulate()` en lugar de `simulate()` para grandes vol\u00famenes.\r\n- `compile()` puede cachearse para reutilizar topolog\u00edas.\r\n- Usa `worker_submit()` para ejecutar tareas en paralelo.\r\n- `hybrid_run()` acepta datos cl\u00e1sicos para reducir overhead cu\u00e1ntico.\r\n\r\n## \ud83d\udca1 Use Cases\r\n\r\n### 1. Quantum Evaluation\r\n\r\nEval\u00faa m\u00faltiples variantes de un circuito para analizar estabilidad y fidelidad:\r\n\r\n```python\r\nscores = sdk.batch_simulate([circuit.to_dict() for _ in range(500)])\r\n```\r\n\r\n### 2. Hybrid Optimization\r\n\r\nIntegra valores cl\u00e1sicos y cu\u00e1nticos:\r\n\r\n```python\r\nresult = sdk.hybrid_run(circuit, classical_inputs={\"alpha\": 0.5, \"beta\": 0.9})\r\n```\r\n\r\n### 3. Distributed Compilation\r\n\r\nDespliega compilaciones pesadas a workers remotos:\r\n\r\n```python\r\nsdk.worker_submit(circuit, backend=\"gcr.io/dsf-quantum-475822/quantum-worker\")\r\n```\r\n\r\n## \ud83d\udcca Rate Limits\r\n\r\n| Tier | Simulations/Day | Batch Size | Worker Jobs | Compilation |\r\n|--------------|-----------------|------------|-------------|-------------|\r\n| Community | 500 | \u274c | \u274c | \u274c |\r\n| Professional | ilimitado | \u2705 \u22641000 | limitado | \u2705 |\r\n| Enterprise | ilimitado | \u2705 \u22641000 | \u2705 | \u2705 (QPU) |\r\n\r\n## \ud83c\udd9a Tier Comparison\r\n\r\n| Feature | Community | Professional | Enterprise |\r\n|---------------------|-----------|--------------|------------|\r\n| Local simulation | \u2705 | \u2705 | \u2705 |\r\n| Batch simulation | \u274c | \u2705 | \u2705 |\r\n| Quantum compilation | \u274c | \u2705 | \u2705 (QPU+) |\r\n| Hybrid evaluation | \u274c | \u2705 | \u2705 |\r\n| Distributed workers | \u274c | \u274c | \u2705 |\r\n| Cloud orchestration | \u274c | \u274c | \u2705 |\r\n\r\n## \ud83d\udcd6 API Reference\r\n\r\n### Initialization\r\n\r\n```python\r\nQuantumSDK(\r\n tier='community'|'professional'|'enterprise',\r\n license_key=None,\r\n base_url=None,\r\n timeout=30\r\n)\r\n```\r\n\r\n### Core Methods\r\n\r\n- `create_circuit()` \u2192 Crea una nueva topolog\u00eda.\r\n- `simulate(circuit)` \u2192 Simula localmente.\r\n- `batch_simulate(circuits)` \u2192 Simula m\u00faltiples circuitos.\r\n- `compile(circuit, target=\"qasm\"|\"binary\"|\"tensor\")` \u2192 Compila el circuito.\r\n- `hybrid_run(circuit, classical_inputs)` \u2192 Eval\u00faa circuito con datos cl\u00e1sicos.\r\n- `worker_submit(circuit, backend, batch_size)` \u2192 Env\u00edo distribuido.\r\n- `worker_status(task_id)` \u2192 Monitorea progreso.\r\n\r\n## \u26a0\ufe0f Common Errors\r\n\r\n| C\u00f3digo | Causa | Soluci\u00f3n |\r\n|------------------------|----------------------------|------------------------------|\r\n| 422 Invalid Circuit | Falta topolog\u00eda o medida | A\u00f1ade al menos una medida |\r\n| 429 Rate Limit | Excediste el l\u00edmite diario | Espera o sube de tier |\r\n| 500 Worker Unavailable | El worker no responde | Reintenta o usa otro backend |\r\n",
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