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Synthetic data ​

Generate synthetic datasets whose every random draw is quantum, with a signed provenance certificate.

Overview ​

The lightrider synthesizer fits a Gaussian copula to your dataset and generates new rows in which every random draw comes from quantum entropy — no pseudo-random fallback. Explore it interactively on the Synthetic data page.

Offline vs. live entropy ​

  • Offline — draws from a bundled IQM QPU bit pool; runs with no network access.
  • Live — draws attested multi-source entropy from the EMS at generation time, so the provenance certificate references verifiable receipts.

Example ​

python
from lightrider import Synthesizer, EntropySource

# Offline: bundled IQM quantum bit pool
synth = Synthesizer(dataset_id="customers_v3").fit(df)
rows  = synth.generate(10_000)          # QRNG-driven synthetic rows

# Live: signed multi-source entropy from the EMS
src   = EntropySource(dataset_id="customers_v3")
synth = Synthesizer(entropy=src).fit(df)
synth.generate(10_000)
synth.manifest.write("customers_v3.provenance.json")

Provenance certificates ​

Every generated dataset ships with a signed manifest recording the dataset id, entropy origin (bundled pool or live EMS receipts), and generation parameters — so downstream consumers can audit where the randomness came from, not just that the data looks plausible.