CQ Catalyst-Q SDK

The Edge-Native Quantum OS for Utility-Scale Applications

Bypass the Hardware. Resolve the Impossible.

Catalyst-Q is the world's first stateless quantum execution proxy. We have replaced localized cryogenic hardware with a software-defined topological architecture. Stop waiting a decade for fault-tolerant hardware—execute massive 2,500-asset risk models and 256-qubit material simulations natively on the edge today.

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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

from catalyst_q import CatalystQClient, QuantumCircuit, RainProtocolKey

client = CatalystQClient()
rain_key = RainProtocolKey.create(workflow_id="bell-demo")

circuit = QuantumCircuit(2).h(0).cx(0, 1).measure(0, 0).measure(1, 1)
request = client.prepare_execute(circuit, rain_key=rain_key, shots=1024, calls_this_month=0)

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

Execute with the built-in transport

from catalyst_q import CatalystQClient, QuantumCircuit, RainProtocolKey
from catalyst_rain import execute_prepared_request

client = CatalystQClient()
rain_key = RainProtocolKey.create(workflow_id="live-bell")
circuit = QuantumCircuit(2).h(0).cx(0, 1).measure(0, 0).measure(1, 1)
request = client.prepare_execute(circuit, rain_key=rain_key, shots=1024, calls_this_month=0)

result = execute_prepared_request(request, timeout=30)
print(result["status_code"], result["latency_ms"], result["response_sha256"])

Run solver helpers

SAT

from catalyst_q import CatalystQClient, RainProtocolKey, SATProblem

client = CatalystQClient()
rain_key = RainProtocolKey.create(workflow_id="sat-demo")
problem = SATProblem(clauses=[[1, -2, 3], [-1, 2]], variables=3)

request = client.prepare_sat(problem, rain_key=rain_key, solver_runs_this_month=0)

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, rain_key=rain_key, solver_runs_this_month=0)

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,
)

Attach RainQRAM preconditioning

from catalyst_q import CatalystQClient, MaxCutProblem, RainProtocolKey, RainQRAM

client = CatalystQClient()
rain_key = RainProtocolKey.create(workflow_id="solver-demo")
qram = RainQRAM.create(workflow_id="solver-memory").store("candidate:greedy", "101010")

request = client.prepare_maxcut(
    MaxCutProblem(edges=[(0, 1, 1.0), (1, 2, 2.0), (0, 2, 0.5)], nodes=3),
    rain_key=rain_key,
    rain_qram=qram,
    solver_runs_this_month=0,
)

Production checklist