qasafetyoperations

Quality Assurance Checklist for AI-Generated Quantum Experiments

bboxqubit

2026-02-07 12:00:00

10 min read

A practical QA checklist for AI-generated quantum experiments: simulator validation, hardware safety gates, reproducibility metadata, and human sign-off.

Hook: Stop AI Slop from Hitting Your Quantum Hardware

AI can accelerate quantum experiment development — but it also amplifies mistakes. In 2026, development teams increasingly use agentic developer tools and agents to generate experiment scripts, and the risk of AI slop — plausible but unsafe or non-reproducible code — is real. If your CI pipeline or a developer agent can submit jobs to a real quantum backend with no checks, you risk wasted queue time, compromised data quality, and reputation damage.

Executive Summary — What this checklist delivers

This article gives a pragmatic, actionable QA checklist for AI-generated quantum experiments. The checklist covers three core areas:

Simulation validation — unit and integration tests against noise-aware simulators and cross-backend checks.
Hardware safety — gating, resource caps, and lifecycle controls to prevent harmful or costly hardware calls.
Human sign-off & audit — reviewer templates, metadata standards, and CI gates for reproducibility and traceability.

Follow these steps to turn AI-generated drafts into production-safe experiments, fit for hybrid quantum-classical CI pipelines in 2026.

Why a tailored QA checklist matters in 2026

Recent trends through late 2025 and early 2026 sharpen the need for discipline:

Cloud quantum providers broadened public access and introduced priority tiers — making job misuse more costly.
Open standards like OpenQASM 3 and richer experiment metadata formats are becoming common; reproducibility expectations are higher.
Agentic developer tools that can write and run code on your desktop or CI are widely deployed — e.g., research previews that grant file-system and API access — increasing automation risk.
Industry adoption of hybrid quantum-classical workflows means experiments are part of larger systems where failures cascade.

Checklist Overview — Quick view

Static analysis & linting of generated code
Simulator validation: deterministic and noise-aware tests
Security & safety checks for backend calls
Resource & quota enforcement
Metadata, seeding, and environment pinning for reproducibility
Human review: experiment sign-off and safety attestation
CI gating and artifact auditing
Post-run validation and provenance logging

Part 1 — Static analysis and structural QA

Before any runtime checks, validate the code structure. AI-generated scripts often suffer from inconsistent style, missing imports, or unsafe patterns.

Actionable checks

Run a linter (e.g., flake8, pylint) and a type checker (mypy) on generated Python/Qiskit/Pennylane code.
Scan for disallowed function calls or patterns (e.g., direct calls to sensitive APIs, exec/eval usage).
Enforce imports from approved SDK versions only; deny use of experimental internal APIs unless flagged.
Verify testable entry points exist: exported functions that accept backend, seed, and config parameters.

Part 2 — Simulation validation (the heart of reproducibility)

Simulators are your safety net. AI can invent circuits that look reasonable but are mathematically invalid or rely on features absent in chosen backends. Testing first against simulators prevents costly hardware mistakes.

Design a layered simulator strategy

Unit tests with statevector or stabilizer simulators for small circuits — assert expected state amplitudes or logical properties.
Noise-aware tests with realistic noise models (provider noise or locally measured calibrations) — assert measurement distributions within tolerance.
Shot-based statistical tests to verify aggregator functions and post-processing pipelines.
Cross-simulator validation (e.g., Qiskit Aer, Pennylane's default, and provider dry-run emulators) to catch backend-specific behavior.

Example test cases

Here are typical assertions to embed as pytest cases. Use deterministic seeds and small circuits for fast CI runs.

# Example (Python/pytest pseudo-code)
def test_bell_state_statevector():
    qc = build_bell_circuit()  # generated script should expose a builder
    sv = run_statevector_simulator(qc, seed=42)
    # Expect |00> and |11> amplitude magnitudes ~ 1/sqrt(2)
    assert approx(abs(sv[0]), rel=1e-6) == 1/math.sqrt(2)
    assert approx(abs(sv[3]), rel=1e-6) == 1/math.sqrt(2)

def test_vqe_noise_aware():
    qc = build_vqe_circuit(params=[0.1,0.2])
    dist = run_noise_simulator(qc, noise_model=provider_noise, shots=1000, seed=7)
    # Check expectation within tolerance of golden baseline
    assert abs(mean_energy(dist) - baseline_energy) < 0.05

Practical tips

Seed everything. Random initializations, transpiler randomness and simulator RNG must be seeded and recorded.
Keep CI simulator runs tiny. Use low-shot smoke tests for CI and reserve larger validation runs for nightly pipelines.
Maintain golden baselines. Store reference outputs (small JSON blobs) and use them in regression checks.

Part 3 — Safety checks for hardware calls

Hardware calls are irrevocable: jobs consume scarce quantum time and can impact calibration windows. Treat them as production deployments with safety interlocks.

Preventative controls

Block direct hardware calls from unreviewed branches: CI must detect and reject scripts that attempt provider.run(backend=real) unless a label/approval exists.
Require an explicit hardware-intent flag in experiment metadata (e.g., "hardware_intent": true) that must be toggled by an authorized reviewer.
Enforce maximum shots and job duration caps in the submission wrapper; translate long-running experiments into multi-stage jobs that request operator approval.
Use scoped service accounts with minimal permissions — avoid broad tokens in CI or agent contexts.

Hardware safety checklist (pre-submission)

Confirm backend compatibility (coupling map, basis gates, max qubits).
Estimate resource cost (shots × queue cost × estimated wait time) and require cost approval above threshold.
Check backend calibration timestamp; disallow runs during scheduled calibrations or maintenance windows.
Verify experiment doesn’t request forbidden calibration-level access or low-level pulse control unless explicitly authorized.
Ensure job cancellation and rollback paths exist; test cancel on dry-run.

Example hardware-gating rule (CI policy)

# Pseudo-policy (enforced in CI validator)
if job.requests_hardware:
    assert PR.has_label('hardware-approved')
    assert PR.approvals >= 2
    assert experiment.metadata['max_shots'] <= 50000
    assert API_TOKEN.scope == 'hardware:submit:limited'

Part 4 — Reproducibility: metadata, environment, and artifacts

To reproduce an experiment years later, you need more than code: you need environment, seeds, backend IDs, provider API version and calibration snapshots.

Minimum metadata to record

Code commit hash and container image digest
Backend identifier, backend version, and calibration timestamp
OpenQASM/OpenPulse source if used; transpiler pass versions
Random seeds and PRNG algorithm name
Dependency lockfile (requirements.txt or pipfile.lock) and Python version
Experiment config: shots, measurement mapping, coupling_map, noise_model used for simulation

Sample metadata JSON

{
  "commit": "a1b2c3d",
  "container": "registry.example.com/quantum-experiments:2026-01-10",
  "backend": "provider.backend.v1",
  "backend_calibration_time": "2026-01-09T22:10:00Z",
  "seed": 42,
  "shots": 2000,
  "noise_model": "provider.noise.v1",
  "transpiler": {"name": "tket", "version": "1.8.0"}
}

Part 5 — Test cases and CI pipeline integration

A well-designed test matrix lets you catch errors early while keeping CI fast. Split tests by level: fast smoke, extended nightly, and pre-hardware staging.

Test tiers

Smoke tests (PR-level): lint, type checks, a handful of deterministic statevector tests and metadata presence checks. Fast — < 2 minutes.
Integration tests (merge-level): noise-aware simulator runs, short shot-based checks, cross-simulator consistency. Run on merge and block deployments.
Staging/hardware dry-run: provider dry-run or emulator run that mimics hardware behavior, plus manual sign-off for hardware submission.
Nightly/weekly full validation: longer runs with larger shot counts and end-to-end reproducibility checks; results archived as artifacts.

CI example: GitHub Actions sketch

# Workflow steps (conceptual)
- name: Lint & Type check
  run: make lint && make typecheck
- name: PR Smoke tests
  run: pytest tests/smoke --maxfail=1 -q
- name: Integration (merge only)
  if: github.ref == 'refs/heads/main'
  run: pytest tests/integration
- name: Hardware approval gate
  if: contains(github.event.pull_request.labels.*.name, 'hardware-approved')
  run: ./scripts/submit_hardware_job.sh

Part 6 — Human sign-off and reviewer playbook

Automated tests catch mechanical issues. Human reviewers are essential to spot scientific or safety concerns that AI may miss.

Reviewer checklist (must be filled before hardware label)

Do the circuit structure and ansatz make physical sense for the stated problem?
Are the noise and backend assumptions documented and reasonable?
Is resource usage (shots, concurrency) justified vs. expected information gain?
Is there a rollback plan and test for job cancellation?
Has at least one subject-matter expert (SME) and one safety/legal reviewer approved the PR?

Sign-off artifact

Require a small YAML sign-off attached to the PR that records approver IDs, timestamp and a short rationale. This becomes part of the experiment archive for audits.

signoff:
  approvers:
    - id: alice@example.com
      role: SME
      time: 2026-01-12T14:05:00Z
    - id: ops@example.com
      role: HardwareOps
      time: 2026-01-12T15:01:00Z
  rationale: "Verified ansatz energy scaling and acceptable 20k shot limit"

Part 7 — Auditing, provenance, and post-run QA

After the run, validate results and keep a complete audit trail to answer future questions about data trustworthiness.

Post-run checks

Compare result distributions to predicted ranges; flag anomalies.
Record actual backend calibration snapshot alongside results.
Store raw measurement data and transformation scripts as immutable artifacts (signed if possible).
Run reproducibility job: replay the experiment in a matched simulator with same seed and verify key metrics within tolerances.

Audit trail best practices

Use append-only logs for submissions with cryptographic signing where compliance requires it.
Attach commit hashes and container digests to result metadata and export to an artifact store (S3, artifact registry).
Keep an audit index with searchable fields: experiment name, submitter, approvers, backend, date, tags.

Advanced strategies and 2026 trends to adopt

As of 2026, teams that combine automated QA with governance see fewer costly hardware incidents. Adopt these advanced practices:

Policy-as-code: codify your hardware gating rules as executable policies (Rego, custom validators) enforced in CI.
Agent sandboxing: Run AI agents that generate code inside ephemeral containers with no hardware creds; require a human to promote artifacts.
Provider dry-run features:
Experiment passports: standardized metadata bundles (interest growing in 2025–2026) that travel with datasets and improve auditability.
Golden benchmarks: maintain small, validated circuit benchmarks to sanity-check new AI-generated experiments.

Short case study (hypothetical but realistic)

A research team used an LLM to generate a suite of VQE experiments to test new ansatz variations. Without QA, one generated script requested 1M shots on a high-priority backend during calibration, blocking other users and returning noisy results. With the checklist above in place, CI blocked the hardware submission, flagged an anomalous shot count, and required two approvers. Engineers reran the experiment in a noise-aware simulator and trimmed shots to an efficient 20k, preserving budget and producing a reproducible result. The audit logs later explained choices to the review board.

Checklist (printable, final version)

Static analysis & type checks passed
Smoke simulator tests pass with deterministic seeds
Noise-aware simulator tests within tolerances
Cross-simulator consistency verified
Experiment metadata populated and committed
Hardware-intent flag present and authorized reviewer assigned
Resource caps validated (shots, duration) and cost approved if above threshold
Service account scoped tokens used; no long-lived secrets in repo
CI policy gates enforced; hardware runs require 2 approvals
Post-run reproducibility verification scheduled and logs archived

Closing: Quick replication template

Use this minimal template to embed into generated scripts so CI and reviewers can immediately validate intent:

# experiment_header.json (embed or produce on run)
{
  "name": "experiment-name",
  "commit": "",
  "hardware_intent": false,
  "max_shots": 10000,
  "seed": 1234
}

"Assume every AI-generated experiment is a draft until validated by simulator checks and a human sign-off."

Final actionable takeaways

Never allow AI agents to submit hardware jobs without human approval and CI gating.
Prioritize fast, deterministic simulation checks in PRs and move heavier validations to merge or nightly pipelines.
Record exhaustive metadata and containerize runs to guarantee reproducibility.
Enforce resource caps and scoped credentials to limit accidental resource consumption.
Keep auditors and SMEs in the loop — sign-off must be recorded as part of the artifact.

Call to action

Start small: add a smoke simulator test and a hardware-intent flag to your generated-experiment template this week. If you want a ready-made starter kit, download our open-source QA scaffolding for AI-generated quantum experiments — it includes CI workflows, policy-as-code examples, and reviewer templates specifically updated for 2026 standards. Visit boxqubit.com/kits to get the starter repo, or email qa@boxqubit.com for a walkthrough.

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