Notebooks to Production: A CI/CD Template for Quantum Experiments Using Marketplace Data

From Notebooks to Production: Why CI/CD for Quantum Experiments Matters in 2026

Hook: If your team treats quantum experiments as isolated Jupyter notebooks, you're losing reproducibility, compliance, and the ability to scale. Organizations in 2026 expect not only working quantum code but auditable pipelines that enforce data marketplace dataset licenses, run unit tests, and gate deployments with benchmarks — all before any job touches real hardware.

Over the last 18 months we've seen a sharp shift: data marketplaces and licensing controls (driven by acquisitions and platform consolidation) now require verifiable provenance for datasets used to train or run quantum ML models. In January 2026, Cloudflare's acquisition of Human Native underscored a market move toward paid, licensed datasets and stronger provenance requirements for downstream workflows. Integrating those checks into CI/CD pipelines is no longer optional — it's a baseline for commercialization and research compliance. For teams operating across hybrid environments, the hybrid edge orchestration playbook is a useful cross-reference for where to run checks and short-lived secrets.

What you'll get from this guide

• Runnable CI/CD template (GitHub Actions) that moves a quantum notebook to production.
• Marketplace-style dataset licensing checks and provenance capture.
• Unit testing and notebook execution strategies for quantum code.
• Performance benchmarking that gates deployment (simulator or hardware).
• Practical scripts and file layout to fork and run today.

Design principles: How to treat quantum experiments like software

Translate software best practices to quantum experiments with three core ideas: reproducibility, provenance, and gating. Reproducibility means your notebook runs headless and deterministic on CI. Provenance and versioning means every input dataset, environment, and commit is recorded and auditable. Gating means automated tests and benchmarks decide whether an experiment may be deployed to hardware or published as a result.

In 2026, quantum stacks matured: common SDKs (Qiskit, PennyLane, Cirq) align on serialization and the community widely uses OpenQASM 3 and QIR for intermediate representation. CI systems must therefore capture the environment (python packages, SDK versions, toolchain) and the IR used to compile circuits for downstream hardware. If you run some steps on-prem or at edge sites, consult the edge cost trade-offs for where to execute heavyweight compilation and simulation.

Repository layout (starter template)

Use this minimal structure. It supports notebook-driven dev, unit tests for algorithmic code, dataset manifests for marketplace checks, and a CI workflow.

# repo layout
  .
  ├── notebooks/
  │   └── experiments/quantum_experiment.ipynb
  ├── src/
  │   ├── algorithm.py
  │   └── utils.py
  ├── tests/
  │   ├── test_algorithm.py
  │   └── test_dataset_manifest.py
  ├── data/
  │   └── dataset_manifest.json
  ├── benchmarks/
  │   └── baseline_metrics.json
  ├── .github/workflows/ci.yml
  ├── Dockerfile
  ├── requirements.txt
  └── run_notebook.py
  

Dataset manifest: marketplace-style licensing checks

Put a small JSON manifest next to data used by notebooks. This manifest is the single source of truth the CI workflow validates. It should include publisher, license identifier, marketplace receipt or contract ID, a cryptographic hash, and an optional allowlist for usage categories.

// data/dataset_manifest.json
  {
    "name": "quantum-training-set-v1",
    "version": "2026-01-01",
    "source": "marketplace://human-native/12345",
    "license": "paid-research-only",
    "receipt": "rcpt_0xABCD1234",
    "sha256": "e3b0c44298fc1c149afbf4c8996fb924...",
    "allowed_uses": ["research", "benchmark"],
    "provenance": {
      "acquired_at": "2026-01-08T12:00:00Z",
      "acquired_by": "alice@example.com"
    }
  }
  

The CI step will parse this manifest and enforce an allowlist. If a dataset is licensed "commercial-only" and your repo's deployment target is labeled "research", the workflow should fail early. For organizations with strict legal boundaries consider mapping manifests to a sovereign cloud or constrained region for storage and image digests.

Notebook execution and unit testing strategy

Running notebooks in CI requires headless execution and deterministic parameters. We recommend a two-step approach:

1. Isolate logic into src/ — put quantum circuit construction and algorithms into Python modules so unit tests can exercise them directly without running the whole notebook.
2. Execute notebooks with papermill in CI for integration tests and to produce artifacts (executed notebook, logs, metrics). Consider also how your artifact storage and provenance tie into marketplace receipts and long-term retention policies documented in your enterprise architecture (see hybrid orchestration and marketplace references above).

Example run_notebook.py uses papermill to parameterize and execute notebooks in CI.

# run_notebook.py
  import papermill as pm
  import argparse

  parser = argparse.ArgumentParser()
  parser.add_argument('--input', required=True)
  parser.add_argument('--output', required=True)
  parser.add_argument('--params', default='{}')
  args = parser.parse_args()

  pm.execute_notebook(
      args.input,
      args.output,
      parameters=eval(args.params),
      kernel_name='python3'
  )
  

Unit tests (pytest)

Keep unit tests focused on circuit generation, cost functions, and numeric stability. Avoid tests that rely on hardware availability — those belong to integration/benchmarking stages.

# tests/test_algorithm.py
  import numpy as np
  from src.algorithm import build_ansatz

  def test_ansatz_shape():
      circ = build_ansatz(num_qubits=4, depth=2)
      assert circ.num_qubits == 4

  def test_energy_evaluation():
      # deterministic pseudo-input
      state = np.zeros(4)
      energy = build_ansatz(4,2).evaluate(state)
      assert isinstance(energy, float)
  

Performance benchmark strategy and gating

Benchmarks in quantum workflows typically measure:

• Execution latency (queue + run time)
• Result quality (fidelity, measurement error mitigated expectation values)
• Resource usage (shots, classical optimization iterations)

We store a baseline in benchmarks/baseline_metrics.json. CI benchmarks compare current run metrics to that baseline and apply threshold gates. You can decide on soft gates (warnings) or hard gates (fail CI). For production deployments to hardware, hard gates are advisable. Many organizations now treat benchmarks as policy to decide whether to run on simulator, edge appliances, or shared hardware.

// benchmarks/baseline_metrics.json
  {
    "experiment": "variational_energy_v1",
    "baseline": {
      "simulator_time_ms": 120,
      "hardware_time_ms": 2000,
      "expected_value": -1.2345,
      "tolerance": 0.05
    }
  }
  

# benchmarks/benchmark.py
  import time
  import json
  from src.algorithm import run_on_simulator

  def benchmark():
      start = time.time()
      result = run_on_simulator(num_qubits=4)
      elapsed = (time.time() - start) * 1000
      metrics = {
          'simulator_time_ms': elapsed,
          'expected_value': result['expectation']
      }
      print(json.dumps(metrics))
      return metrics

  if __name__ == '__main__':
      benchmark()
  

Gating logic example

A simple gate compares the metric to baseline and returns non-zero exit when out-of-bounds.

# .github/workflows/gate.py (called in CI)
  import json
  import sys

  def gate(metrics, baseline):
      err = abs(metrics['expected_value'] - baseline['expected_value'])
      if err > baseline['tolerance']:
          print(f"Fail: expectation {metrics['expected_value']} differs > tolerance {baseline['tolerance']}")
          sys.exit(2)
      print('Pass gating')

  if __name__ == '__main__':
      metrics = json.load(open('artifacts/current_metrics.json'))
      baseline = json.load(open('benchmarks/baseline_metrics.json'))['baseline']
      gate(metrics, baseline)
  

Provenance capture

Capture minimal provenance artifacts in CI and attach them to builds or artifacts storage:

• Commit SHA and branch
• Executed notebook (HTML) with output
• Dataset manifest (copied) and dataset checksum
• Environment hash (pip freeze or conda-lock) and Docker image digest
• Benchmark metrics JSON

Provenance examples are critical for reproducibility and legal audits when dataset licenses are complex or when marketplaces require usage reporting. In enterprise settings, pipeline runs should attach receipts (marketplace transaction IDs) to the build metadata. Pairing provenance with a versioning and governance approach reduces disputes and speeds audits.

Complete GitHub Actions CI template (runnable)

Below is a practical, runnable CI YAML you can drop into .github/workflows/ci.yml. It performs dataset checks, runs unit tests, executes the notebook with papermill, runs benchmarks on a simulator, and gates deployment.

name: Quantum Notebook CI

on:
  push:
    branches: [ main ]
  pull_request:
    branches: [ main ]

jobs:
  build-and-test:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout
      uses: actions/checkout@v4

    - name: Set up Python
      uses: actions/setup-python@v4
      with:
        python-version: '3.11'

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r requirements.txt
        pip install papermill pytest

    - name: Validate dataset manifest (marketplace check)
      run: |
        python - <<'PY'
        import json, sys
        manifest = json.load(open('data/dataset_manifest.json'))
        allowed = ['research','benchmark']
        if manifest.get('license') == 'paid-research-only' and 'research' not in manifest.get('allowed_uses',[]):
            print('Dataset license disallows research use')
            sys.exit(1)
        print('Dataset manifest validated')
        PY

    - name: Run unit tests
      run: |
        pytest -q

    - name: Execute notebook (integration test)
      run: |
        python run_notebook.py --input notebooks/experiments/quantum_experiment.ipynb \
          --output artifacts/executed_notebook.ipynb --params "{'shots':1024}"

    - name: Benchmark simulator
      run: |
        python benchmarks/benchmark.py > artifacts/current_metrics.json

    - name: Gate against baseline
      run: |
        python .github/workflows/gate.py

    - name: Upload artifacts
      uses: actions/upload-artifact@v4
      with:
        name: ci-artifacts
        path: |
          artifacts/executed_notebook.ipynb
          artifacts/current_metrics.json
          data/dataset_manifest.json

  deploy:
    needs: build-and-test
    runs-on: ubuntu-latest
    if: success()
    steps:
      - name: Deploy to staging (example)
        run: echo "Deploying to staging environment..."
  

Extending the template for hardware runs and enterprise policies

For hardware runs, add a separate job that only executes when gating passes and when secrets (API keys) are present. Use short-lived credentials and rotate them via your secrets manager. Attach a final provenance bundle containing the hardware job ID, provider, and receipts back to your artifact store. Patterns from the hybrid orchestration playbook help when hardware runs span cloud and on-prem sites.

For enterprise marketplace integrations, replace the manifest validation step with a call to the marketplace API to verify receipts and usage rights. Example (pseudo-cURL):

curl -X POST https://marketplace.example.com/api/verify-receipt \
  -H "Authorization: Bearer $MARKETPLACE_TOKEN" \
  -d '{"receipt":"rcpt_0xABCD1234","usage":"research"}'
  

2026 trends that change how you build these pipelines

• Stronger dataset marketplaces: As of early 2026, several platform moves (e.g., Cloudflare acquiring Human Native in Jan 2026) accelerated the expectation of verifiable dataset licensing. Pipelines must record receipts and enforce license allowlists.
• Unified IR & tooling: Widespread adoption of OpenQASM 3 and QIR in late 2024–2025 has made serialization and portability easier; ensure your pipeline captures the IR used to compile circuits for hardware. See the storage and hardware discussion in NVLink/RISC-V analysis for implications on artifact movement.
• Benchmarks as gates: Organizations are using performance baselines as hard gates to hardware. Expect legal and procurement groups to demand these artifacts for audit trails.
• Shift-left reproducibility: Teams prefer running more of the stack in CI (simulators, noise models) to catch regressions earlier and to reduce expensive hardware iterations. If you operate distributed test runners, consult hybrid and edge orchestration patterns for runner placement (hybrid edge orchestration).

"Provenance and licensing are as important as algorithm correctness for production quantum workflows in 2026."

Operational checklist before production deployment

1. Enforce dataset manifests and verify marketplace receipts in CI.
2. Keep algorithmic logic in modular Python packages for testability.
3. Execute notebooks with papermill to produce reproducible artifacts.
4. Store baseline benchmarks and gate aggressively for hardware access.
5. Capture and store full provenance (environment, receipts, commit SHA, executed notebooks).
6. Use ephemeral credentials for hardware provider access and rotate them automatically. For sovereignty and policy constraints, map secrets lifecycles as described in sovereign cloud architectures like hybrid sovereign cloud.

Real-world example: A quick case study

A mid-sized quantum research team migrated a notebook-based VQE experiment into CI in late 2025. They enforced dataset manifests for a commercially-licensed training set, modularized circuit-generation code, and added a simulator-based benchmark. Within two weeks they reduced hardware runs by 60% because many regressions were caught in CI. When the company later audited usage of a marketplace dataset, the team presented a consistent trail: receipt, manifest, executed artifacts, and benchmark metrics — which satisfied the vendor's licensing compliance checks.

Troubleshooting and tips

• If notebooks intermittently fail on CI, lock dependencies (use pip-compile or conda-lock) and use Docker images with pinned digests to ensure deterministic environments. Consider where images live and how digests are resolved across regions when following a sovereign cloud model.
• For nondeterministic quantum results, use statistical tests in gating (e.g., run 3 repeats and use confidence intervals) rather than strict equality.
• Keep secrets out of logs. Mask API keys and marketplace tokens. Prefer your CI's secrets store.
• Store artifacts in a centralized artifact registry (S3, Azure Blob) and tag with metadata for fast retrieval during audits.

Next steps: Fork, extend, and integrate

This template is intentionally minimal to be runnable immediately. Fork it, replace the benchmark with your own metric (fidelity, energy, or error mitigation performance), and plug in your marketplace verification step. If you use an enterprise data marketplace, integrate their API call in the manifest validation stage and treat receipts as first-class governance artifacts connected to your governance process.

For teams evaluating platforms: look for providers that emit stable IR (OpenQASM 3/QIR), provide programmatic receipts for datasets, and support short-lived credentials for hardware jobs. These capabilities make CI/CD automation practical and auditable.

Actionable takeaways

• Start by extracting deterministic logic from notebooks into src/ so unit tests can run quickly in CI.
• Add a dataset manifest and validate receipts during CI to enforce marketplace licensing.
• Use papermill to execute notebooks headlessly and generate artifacts for provenance.
• Define baseline metrics and gate hardware access with benchmark-based CI checks. When deciding where to run simulation vs hardware, review edge-oriented trade-offs.

Call to action

Want a ready-to-run repository and an enterprise checklist tailored to your stack? Download the template, run the GitHub Actions workflow on your repo, and contact qbitshared.com for integration consulting. We'll help you extend the gating rules for your marketplace contracts and automate hardware provisioning with auditable provenance.

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