CQ Catalyst-Q SDK

Exact Quantum and Optimization Execution for Production Teams

Submit Standard Inputs. Get Exact Results.

Catalyst-Q is a breakthrough simulator API and Python SDK for QASM circuits, SDK circuit objects, and optimization model payloads. Build with the inputs your team already uses, then validate outputs with reproducible benchmark artifacts.

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pip install catalyst-q

# Controlled hosted index:
pip install --index-url https://catalyst-q-sdk.strategic-innovations.ai/simple catalyst-q

Install

Use the public PyPI name after release. The Cloudflare simple index remains available for controlled distribution.

pip install catalyst-q

# Controlled hosted index:
pip install --index-url https://catalyst-q-sdk.strategic-innovations.ai/simple catalyst-q

Accountless start

The client creates a local anonymous install ID on first SDK use and sends it with each request as X-Catalyst-Install-ID. Registration and limits are enforced by the API.

Execute a circuit object

from catalyst_q import CatalystQClient, QuantumCircuit

client = CatalystQClient()

circuit = QuantumCircuit(2).h(0).cx(0, 1).measure(0, 0).measure(1, 1)
request = client.prepare_execute(circuit, workflow_id="bell-demo", shots=1024)

# Send request.method, request.url, request.headers, and request.json with your HTTP client.

Execute QASM

from catalyst_q import CatalystQClient

client = CatalystQClient()

qasm = """
OPENQASM 2.0;
qreg q[2];
creg c[2];
h q[0];
cx q[0],q[1];
measure q[0] -> c[0];
measure q[1] -> c[1];
"""

request = client.prepare_qasm(qasm, workflow_id="bell-qasm", shots=1024)

Run solver helpers

SAT

from catalyst_q import CatalystQClient, SATProblem

client = CatalystQClient()
problem = SATProblem(clauses=[[1, -2, 3], [-1, 2]], variables=3)

request = client.prepare_sat(problem, workflow_id="sat-demo")

DAG Optimization

from catalyst_q import CatalystQClient, MaximumWeightClosureProblem

# Optimize hierarchical structures (e.g., taxonomies, decision trees)
problem = MaximumWeightClosureProblem(
    node_weights=[10.0, 5.0, 20.0, 15.0],
    dependencies=[(2, 0), (3, 1), (3, 2)]
)
request = client.prepare_dag_closure(problem, workflow_id="dag-demo")

Quantum Error Correction (QECL)

from catalyst_q import CatalystQClient

client = CatalystQClient()

# Simulate surface code at distance 5 with 0.001 error rate
request = client.prepare_qecl(
    code="surface",
    distance=5,
    error_rate=0.001,
    rounds=10,
    qubits=100,
    workflow_id="qecl-demo",
)

Usage preflight

activation = client.prepare_activation_request()
usage = client.prepare_usage_check_request(
    operation="execute",
    route="/v3turbo/execute",
    billing_estimate=request.json["billing_estimate"],
    production=False,
)

Optimization model payloads

from catalyst_q import CatalystQClient, MaxCutProblem

client = CatalystQClient()

request = client.prepare_maxcut(
    MaxCutProblem(edges=[(0, 1, 1.0), (1, 2, 2.0), (0, 2, 0.5)], nodes=3),
    workflow_id="maxcut-demo",
)

Production checklist