Worked private workflow: noisy Floquet Ising chain

Website downloads: current importable bundle, mathematical specification, and Private Workflow Development Guide. This is the current guide companion. The video’s retained version-one/version-two demonstration study and its recorded outputs remain a separate historical example; their observables and evidence must not be relabeled as this bundle’s executions.

The complete importable example is documentation/workflow_examples/qiskit_floquet_spin_chain.json, relative to the release root. Its embedded main.py is the tested Qiskit program. This directory's SPECIFICATION.md defines the exact science and controls; HISTORICAL_VALIDATION.json records the earlier 16-case comparison and generation provenance.

Use the bundle

1. Have the operator provision the bundle's exact dependencies in the selected private-workflow executor: Qiskit 2.5.2 and NumPy 2.3.5. Requirements declarations do not install them.
2. Open My workflows, choose Import bundle, and select the JSON file. Import creates a private proposal; it does not activate or execute the program.
3. Inspect the source, mathematical specification, parameters and declared checks. Run Validate, review the numerical check evidence, then use the end-user Activate control if satisfied.
4. Run the defaults for six qubits and eight cycles, or select eight qubits and 16 cycles for the documented artifact example. Review the metrics/curves and download observables.csv and final_states.json for complete data.

Use the AI authoring prompt in the development guide and this mathematical specification to request a newly generated proposal. The Polynomial starter supplies the bundle interface; the scientific algorithm must be generated from the specification. Model output is subject to review and validation, and may require correction. The shipped tested bundle is also available for direct import.

Inspect the embedded Python source

Run this from the release root in Python. It extracts to a new directory and refuses to overwrite an existing main.py:

import json
from pathlib import Path

bundle_path = Path("documentation/workflow_examples/qiskit_floquet_spin_chain.json")
bundle = json.loads(bundle_path.read_text(encoding="utf-8"))
program = next(item["content"] for item in bundle["files"] if item["path"] == "main.py")
destination = Path("floquet_source_review")
destination.mkdir(exist_ok=True)
with (destination / "main.py").open("x", encoding="utf-8", newline="") as stream:
    stream.write(program)

The source is embedded in the bundle to keep the example directly importable. Editing an extracted copy does not change the bundle; update its files[].content, preserve matching output declarations, and import a new private version when you intend to use edits.

Developer verification

The supplied helper runs only this hash-pinned example in the local Python environment. It is a developer check, not the production Docker execution route. With the exact dependencies installed, run from the release root:

python release_tools/validate_lifecycle_workflow_guide.py --output floquet_guide_validation.json

It checks the exact JSON node/depth/byte boundaries, bundle/source identity and schema; executes default six-qubit/eight-cycle, eight-qubit/16-cycle and three declared analytic cases; verifies state-file pointers, arrays, CSV/series consistency and physical invariants; and compares the two nontrivial metric sets with the prior independent reference values. It uses temporary output directories and writes a fresh JSON report to your chosen location. It does not install dependencies or use an external AI provider. The earlier full-state independent comparison remains explicitly historical evidence.