Catalyst-Q

Exact answers you can re-check — for quantum circuits and hard optimization, with replayable evidence attached.

Catalyst-Q is a Python SDK + hosted API for exact, certificate-backed quantum-circuit execution and combinatorial optimization. Every result ships with a re-runnable proof artifact (a .rain certificate): a number you can verify yourself, not a black-box guess. Implementation details are intentionally not distributed in this package; what you get is a clean client for building.

pip install catalyst-q

What it's good at

1. Exact circuit execution with replayable evidence. Catalyst-Q answers the observable you ask for — an amplitude, a basis-state probability, a Pauli expectation, or a sampled distribution — through hosted, certificate-backed execution. On fixed public benchmark cases (SuperMarQ, QED-C, QASMBench, MQT) the SDK emits exact results and deterministic artifacts you can re-run and inspect. The public package documents the request/response contract, evidence format, and benchmark harness; proprietary implementation details stay server-side.

2. Optimization with a proof, not a promise. QUBO, SAT, TSP, Max-Cut, Portfolio, Knapsack, Vehicle Routing, Unit Commitment, and DAG optimization — solved to an exact certificate where provable, or used as a challenger / MIP-start generator on hard, time-limited operational models. You get the answer and the evidence.

3. Chemistry-grade VQE. Ground-state energies validated to far below chemical accuracy on the fixed proof cases (e.g. H₂/STO-3G), with the variational parameter count held flat as system size grows. Evidence ships as deterministic JSON/Markdown artifacts.

4. Everything is re-runnable. The proof and benchmark harnesses emit deterministic artifacts with public baselines, result hashes, and latency/size telemetry. Claims are tied to generated evidence for the fixed cases — not asserted as theorems about all workloads.

Honest scope

Catalyst-Q is exact and memory-bounded for the published benchmark circuit families; the main variable that scales is time, and it scales with the circuit's real structure — fast for structured, bounded-treewidth, and low-magic circuits at large scale, and slower (worst-case exponential, as for any exact method) for fully volume-law random circuits. It makes no claims of Shor's algorithm, breaking cryptography, broad hardware-advantage claims, or solving NP in general. Benchmark numbers refer to the specific generated artifacts.

Install

pip install catalyst-q

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

Free developer tier: full public gate set up to 100 qubits for evaluation, with 10 hosted API executions/month and 100 compute credits/month. Production use requires a paid server-side license. Private/offline/local deployment requires custom licensing — contact Strategic Innovations AI.

Quickstart

Run a circuit (SDK objects)

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", shots=1024)
# send request.method / request.url / request.headers / request.json with your HTTP client

Run a circuit (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)

Solve an optimization model (with a certificate)

from catalyst_q import CatalystQClient, MaxCutProblem

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

Accepted inputs: QASM / OpenQASM circuit text, SDK circuit objects, and SAT / TSP / Knapsack / Portfolio / QUBO / Max-Cut / DAG / VRP / Unit-Commitment payloads — plus JSON-ready request objects for direct HTTP clients and CI harnesses.

Prove it yourself

catalyst-q-prove   --output-dir catalyst-q-proof-results      # deterministic TSP + VQE evidence
catalyst-q-benchmark --execute-api \
  --base-url https://api.strategic-innovations.ai/v3turbo \
  --output-dir catalyst-q-live-api-benchmarks                 # latency / bytes / status / sha256 per request

Both emit deterministic JSON + Markdown artifacts with public baselines, result hashes, and benchmark-limited claim language — evidence for the fixed cases, re-runnable by you.

Operational add-ons

• Solver Rescue — catalyst-q-rescue / catalyst-q-rescue-copilot: use Catalyst-Q as a challenger and MIP-start (.mst) generator for hard, time-limited models; emits an executive comparison report (feasible rescues, incumbent improvements, gap, runtime, estimated value) with a Markdown audit trail. The copilot path is fully offline.
• HD-QML (EQCM) — EQCMPolicyBuilder in catalyst_rain: build a QUBO landscape (up to ~800 variables) from edge-side resonance scores and ship it to the cloud solver in milliseconds.
• ATC Flow Replay — catalyst-q-atc-flow: train a small adapter from a knowledge pack and run shadow-mode air-traffic-flow decision support for human review. Not live ATC, separation assurance, or clearance issuance; every plan carries a hard human_review_required status.

Licensing

• Developer Free — $0 for development and evaluation only; 100 qubits, 10 hosted API executions/month, and 100 compute credits/month.
• Starter Production — $99/month for one production app or pilot; 250 qubits and 2,000 compute credits/month.
• Team Pro — $399/month for team workflows and CI; 500 qubits and 12,000 compute credits/month.
• Scale — $1,500/month and up for embedded production use; 2,000 qubits and 75,000 compute credits/month.
• Private / offline / local — custom licensing only.

Credits scale with qubit level, circuit depth, shot count, solver problem size, and replayable evidence workflows. Local validation and malformed-request preflight checks should not consume credits; accepted hosted executions do.

Pricing: https://catalyst-q-sdk.strategic-innovations.ai/docs/pricing