Marqov SDK

Orchestration engine for hybrid quantum-classical workflows.

Run a Bell state immediately — no credentials, no infrastructure:

import asyncio
from marqov.circuits import Circuit
from marqov.executors import LocalExecutor

async def main():
    result = await LocalExecutor().execute(
        Circuit().h(0).cnot(0, 1), shots=1000
    )
    print(result.counts)  # {'00': ~500, '11': ~500}

asyncio.run(main())

Scale to parallel workflows across any backend:

from marqov import task, workflow, bell_state
from marqov.executors import LocalExecutor

@task
async def measure(shots):
    result = await LocalExecutor().execute(bell_state(), shots=shots)
    return result.counts

@workflow
def multi_shot_study(shot_counts):
    return [measure(n) for n in shot_counts]  # all run in parallel

dispatch = multi_shot_study([100, 500, 1000, 5000])
# dispatch.run(client) — needs a Temporal worker
# Use the Marqov platform or run your own: see marqov/workflows/

Independent tasks execute in parallel automatically. Marqov handles scheduling, retries, and result collection across any supported backend.

Installation

pip install marqov

With backend-specific extras:

# IBM Quantum
pip install "marqov[qiskit]"

# All extras
pip install "marqov[all]"

For local development:

git clone https://github.com/marqov-dev/marqov-sdk
cd marqov-sdk
pip install -e ".[all,dev]"
pytest tests/ -v

Cloud Executors

Swap in a cloud backend when you're ready to run on hardware — on your own provider accounts, no Marqov account needed:

import asyncio
from marqov.circuits import Circuit
from marqov.executors import ExecutorFactory

async def main():
    circuit = Circuit().h(0).cnot(0, 1)

    executor = ExecutorFactory.create_executor("sv1", {
        "provider": "AWS Braket",
        "device_arn": "arn:aws:braket:::device/quantum-simulator/amazon/sv1",
        "s3_bucket": "my-bucket",
        "s3_prefix": "jobs",
    })
    result = await executor.execute(circuit, shots=1000)
    print(result.counts)

asyncio.run(main())

Or run directly on IonQ hardware via the native REST API (no AWS account needed):

executor = ExecutorFactory.create_executor("qpu.aria-1", {
    "provider": "IonQ Direct",
    "api_key": "your-ionq-api-key",  # or set IONQ_API_KEY
})
result = await executor.execute(circuit, shots=1000)

Or run on Rigetti QPUs (or the local QVM, no cloud account needed) via Rigetti QCS:

executor = ExecutorFactory.create_executor("2q-qvm", {
    "provider": "Rigetti QCS",
})
result = await executor.execute(circuit, shots=1000)

Supported Backends

Circuit Interop

Circuit is a backend-agnostic abstraction that converts to any supported framework's native format:

from marqov.circuits import Circuit

circuit = Circuit().h(0).cnot(0, 1)

circuit.to_qiskit()   # qiskit.QuantumCircuit
circuit.to_braket()   # braket.circuits.Circuit
circuit.to_cirq()     # cirq.Circuit
circuit.to_pyquil()   # pyquil.Program  (requires pip install marqov[pyquil])

Import from other formats:

circuit = Circuit.from_qiskit(qiskit_circuit)
circuit = Circuit.from_cirq(cirq_circuit)
circuit = Circuit.from_pennylane(tape)
circuit = Circuit.from_pyquil(pyquil_program)  # requires pip install marqov[pyquil]

Using the hosted platform (marqov.platform)

The SDK runs fully standalone — everything above needs no Marqov account.

If you want managed backend credentials, persistent job history, execution traces, and spend controls without running your own infrastructure, the Marqov Platform is an opt-in value-add.

marqov.platform is an optional import — loading marqov never loads the platform client. It is only activated when you import it explicitly.

Live-server caveat: The examples below are not yet verified against a live server — live verification is pending our staging environment.

v1.0 scope: v1.0 supports free backends (e.g. dwave-sim). Paid backends and Circuit submission are coming in a future update.

Quickstart

1. Set your API key (get one from the Marqov Platform dashboard):

export MARQOV_PLATFORM_KEY="marqey_live_your_key_here"

2. Submit a script and poll for results:

from marqov.platform import MarqovClient

# Key is read from MARQOV_PLATFORM_KEY automatically
client = MarqovClient()

script = """
from marqov import task

@task
async def bell(shots):
    from marqov.circuits import Circuit
    from marqov.executors import LocalExecutor
    result = await LocalExecutor().execute(
        Circuit().h(0).cnot(0, 1), shots=shots
    )
    return result.counts

bell(1000)
"""

job = client.submit(script, backend="dwave-sim", framework="marqov", shots=1000)
print("Job ID:", job.id)

# Block until complete (up to 5 minutes by default)
result = job.result(timeout=300.0)
print(result.counts)       # e.g. {'00': 507, '11': 493}
print(result.probabilities) # e.g. {'00': 0.507, '11': 0.493}

3. Check available backends:

for b in client.backends():
    print(b.slug, b.name, "available:", b.is_available)

4. Reconnect to a job from a previous session:

job = client.job("550e8400-e29b-41d4-a716-446655440000")
result = job.result(timeout=60.0)

Error handling

All platform errors inherit from MarqovPlatformError:

from marqov.platform import AuthenticationError, JobFailed, RateLimited

try:
    job = client.submit(script, backend="dwave-sim", framework="marqov")
    result = job.result(timeout=120.0)
except AuthenticationError:
    print("Check your MARQOV_PLATFORM_KEY")
except JobFailed as e:
    print("Job failed:", e.message)
except RateLimited as e:
    print(f"Rate limited — retry after {e.retry_after}s")
except TimeoutError:
    print("Timed out — job is still running server-side")

For the full error taxonomy and retry guidance see docs/platform-client/error-handling.md.

Platform documentation

• Getting started
• Error handling
• API reference

Contributing

See CONTRIBUTING.md for the executor interface spec, canonical gate set, factory registration steps, and local QVM setup for Rigetti development.

Bounty issues are open through unitaryHACK 2026 — see the issues page for what's available.

License

Apache 2.0