Technical references for benchmark methodology, rules, and reproducibility. The full suite is open source — all scripts, specs, and data are in the GitHub repository.
Run your first benchmark entry with three commands:
git clone https://github.com/qencode-benchmark/qencode-benchmark pip install -r requirements-v4.txt python scripts/generate_entry_v4.py \ --molecule H2 --mapping jordan_wigner \ --ansatz-type uccsd --out-dir releases/v4/db
Output: a signed JSON entry in releases/v4/db/ with PySCF reference energies, VQE result, circuit metrics, and a SHA-256 provenance hash. Runs on any machine with Python 3.11. GPU backend available with --backend lightning.gpu.
Run your first entry in under 10 minutes. Covers environment setup, generate_entry_v4.py, and entry verification.
Machine-readable spec: geometry, active spaces, supported mappings, exclusion rules, and certification criteria.
Eligibility, accuracy ranking, hardware cost ranking, balanced score formula, and research tab policy.
Pinned environment: PySCF 2.5.0, PennyLane 0.45, openfermion 1.7.1. Install with pip install -r requirements-v4.txt.
How to cite QEncode in papers and grant applications.
Open-source licensing terms for using, modifying, and distributing QEncode.
Need managed certification?
If you need signed artifacts for a paper, grant, or hardware evaluation, apply for managed certification. The self-run path is always free.
