Quantum Version Control: Managing Quantum Code and Experiment Reproducibility

Table of Contents

1. Introduction
2. Why Version Control Matters in Quantum Computing
3. Challenges Unique to Quantum Development
4. Classical Version Control Tools for Quantum Code
5. Using Git for Quantum Circuit Codebases
6. Versioning Quantum Circuit Definitions
7. Tracking Parameterized Circuits and Variants
8. Managing Simulation vs Hardware Code Paths
9. Logging Measurement Outcomes and Metadata
10. Hashing Circuit Diagrams for Integrity
11. Integrating with Jupyter Notebooks
12. Storing and Versioning Circuit Snapshots
13. Circuit JSON or QASM Archives
14. Artifact Tracking: Results, States, and Errors
15. Managing Backend Configuration and Noise Models
16. Collaborative Development with Quantum Pipelines
17. Using DVC (Data Version Control) for Experiments
18. GitHub Actions for Automated Validation
19. Best Practices for Quantum Reproducibility
20. Conclusion

1. Introduction

As quantum computing research and software projects grow in complexity, version control becomes essential not only for code management but also for preserving experimental results, circuit states, and hardware configurations.

2. Why Version Control Matters in Quantum Computing

• Quantum software is highly experimental
• Measurement outputs are stochastic
• Hardware configurations and noise change frequently
• Reproducibility of results is a key concern

3. Challenges Unique to Quantum Development

• Circuits often live in-memory and are not stored as source files
• Classical and quantum components are tightly coupled
• Simulation and real device execution differ

4. Classical Version Control Tools for Quantum Code

Standard tools like Git remain foundational:

• Track .py, .ipynb, .qs, .qasm files
• Document gate logic and transpilation parameters

5. Using Git for Quantum Circuit Codebases

• Organize by circuit modules
• Track evolution of logic and measurement strategy
• Use meaningful commit messages and tags:

  git tag vqe-exp-2024.05

6. Versioning Quantum Circuit Definitions

Save canonical circuits as QASM or Quil files:

circuit.qasm(filename='grover.qasm')

7. Tracking Parameterized Circuits and Variants

Use parameter IDs, seeds, and configurations:

params = {'theta': 0.2, 'depth': 4, 'seed': 42}

Log alongside job outputs.

8. Managing Simulation vs Hardware Code Paths

Split simulator and QPU code explicitly:

if use_simulator:
    backend = Aer.get_backend('qasm_simulator')
else:
    backend = provider.get_backend('ibmq_belem')

9. Logging Measurement Outcomes and Metadata

Store .json or .csv result files per run:

• Counts
• Timestamp
• Backend
• Shot number

10. Hashing Circuit Diagrams for Integrity

Use circuit hash to track changes:

import hashlib
hashlib.sha256(circuit.qasm().encode()).hexdigest()

11. Integrating with Jupyter Notebooks

Use notebook checkpoints and export circuit states into separate files:

nbconvert --to script notebook.ipynb

12. Storing and Versioning Circuit Snapshots

Store transpiled versions and draw outputs:

• .qasm
• .txt (ASCII circuit)
• .png (drawn diagrams)

13. Circuit JSON or QASM Archives

Store circuit metadata and logic together:

{
  "circuit": "...",
  "params": {"theta": 0.3},
  "backend": "qasm_simulator",
  "shots": 1024
}

14. Artifact Tracking: Results, States, and Errors

Use structured directories:

/experiments/grover/v1/
/results/counts.json
/metadata/run_config.yaml

15. Managing Backend Configuration and Noise Models

Capture backend snapshot:

backend.configuration().to_dict()
backend.properties().to_dict()

16. Collaborative Development with Quantum Pipelines

Integrate GitHub, GitLab, or Azure DevOps:

• Use branches for experiments
• Track team contributions
• Merge pipelines carefully

17. Using DVC (Data Version Control) for Experiments

DVC helps version result files and model checkpoints:

dvc add counts.csv
dvc push

18. GitHub Actions for Automated Validation

Run simulators and compare outputs on push:

- name: Run Qiskit test
  run: python test_circuits.py

19. Best Practices for Quantum Reproducibility

• Always log backend name and timestamp
• Store QASM or circuit diagrams
• Track qubit mappings and transpiler settings
• Separate parameter sets and results
• Include noise models when simulating

20. Conclusion

Effective version control in quantum software extends beyond source code. It must encompass the full lifecycle of quantum development—from circuit construction to hardware configuration and result logging. By implementing structured workflows, you can ensure reproducibility, traceability, and collaboration across teams and platforms.