Datasets:

Aevion-Verifiable-AI
/

quantum-governance-benchmark

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
benchmark_id: string
benchmark_name: string
cards_referenced: list<item: string>
  child 0, item: string
claim_boundary: struct<blocked_claims: list<item: string>, what_this_benchmark_does_NOT_prove: list<item: string>, w (... 46 chars omitted)
  child 0, blocked_claims: list<item: string>
      child 0, item: string
  child 1, what_this_benchmark_does_NOT_prove: list<item: string>
      child 0, item: string
  child 2, what_this_benchmark_proves: list<item: string>
      child 0, item: string
cross_vendor_contrast: struct<bell_simulator_vs_hardware: struct<hardware_correlation: double, interpretation: string, nois (... 46 chars omitted)
  child 0, bell_simulator_vs_hardware: struct<hardware_correlation: double, interpretation: string, noise_gap: double, simulator_correlatio (... 10 chars omitted)
      child 0, hardware_correlation: double
      child 1, interpretation: string
      child 2, noise_gap: double
      child 3, simulator_correlation: double
description: string
hf_org: string
hf_user: string
promotion_state: string
public_claim: bool
publisher: string
reproducibility: struct<dependencies: list<item: string>, ibm_runner: string, origin_runner: string, schema: string,  (... 14 chars omitted)
  child 0, dependencies: list<item: string>
      child 0, item: string
  child 1, ibm_runner: string
  child 2, origin_runner: string
  child 3, schema: string
  child 4, tests: string
results: struct<ibm_marrakesh_bell: struct<backend: string, backend_qubits: int64, card: str
...
tion_score: string, hardware_note: string, leakage_score: string, promotion_threshold: (... 32 chars omitted)
  child 0, correlation_score: string
  child 1, hardware_note: string
  child 2, leakage_score: string
  child 3, promotion_threshold: string
  child 4, tvd_from_ideal: string
timestamp: timestamp[s]
version: string
whitespace_analysis: struct<blue_ocean_positioning: struct<analogy: string, what_aevion_is: string, what_aevion_is_not: s (... 162 chars omitted)
  child 0, blue_ocean_positioning: struct<analogy: string, what_aevion_is: string, what_aevion_is_not: string, why_now: string>
      child 0, analogy: string
      child 1, what_aevion_is: string
      child 2, what_aevion_is_not: string
      child 3, why_now: string
  child 1, category: string
  child 2, novelty_claims: list<item: struct<backed_by: list<item: string>, claim: string, evidence: string, uniqueness: string (... 2 chars omitted)
      child 0, item: struct<backed_by: list<item: string>, claim: string, evidence: string, uniqueness: string>
          child 0, backed_by: list<item: string>
              child 0, item: string
          child 1, claim: string
          child 2, evidence: string
          child 3, uniqueness: string
row_count_band: string
parquet_mirror_ref: string
hf_dataset_id: string
schema_version: string
parquet_mirror_note: string
simulator_only: bool
claim_ceiling: string
hf_dataset_url: string
public_claim_permitted: bool
hardware_correctness_claim_permitted: bool
license: string
to
{'schema_version': Value('string'), 'hf_dataset_id': Value('string'), 'hf_dataset_url': Value('string'), 'parquet_mirror_ref': Value('string'), 'parquet_mirror_note': Value('string'), 'row_count_band': Value('string'), 'license': Value('string'), 'simulator_only': Value('bool'), 'hardware_correctness_claim_permitted': Value('bool'), 'public_claim_permitted': Value('bool'), 'claim_ceiling': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              benchmark_id: string
              benchmark_name: string
              cards_referenced: list<item: string>
                child 0, item: string
              claim_boundary: struct<blocked_claims: list<item: string>, what_this_benchmark_does_NOT_prove: list<item: string>, w (... 46 chars omitted)
                child 0, blocked_claims: list<item: string>
                    child 0, item: string
                child 1, what_this_benchmark_does_NOT_prove: list<item: string>
                    child 0, item: string
                child 2, what_this_benchmark_proves: list<item: string>
                    child 0, item: string
              cross_vendor_contrast: struct<bell_simulator_vs_hardware: struct<hardware_correlation: double, interpretation: string, nois (... 46 chars omitted)
                child 0, bell_simulator_vs_hardware: struct<hardware_correlation: double, interpretation: string, noise_gap: double, simulator_correlatio (... 10 chars omitted)
                    child 0, hardware_correlation: double
                    child 1, interpretation: string
                    child 2, noise_gap: double
                    child 3, simulator_correlation: double
              description: string
              hf_org: string
              hf_user: string
              promotion_state: string
              public_claim: bool
              publisher: string
              reproducibility: struct<dependencies: list<item: string>, ibm_runner: string, origin_runner: string, schema: string,  (... 14 chars omitted)
                child 0, dependencies: list<item: string>
                    child 0, item: string
                child 1, ibm_runner: string
                child 2, origin_runner: string
                child 3, schema: string
                child 4, tests: string
              results: struct<ibm_marrakesh_bell: struct<backend: string, backend_qubits: int64, card: str
              ...
              tion_score: string, hardware_note: string, leakage_score: string, promotion_threshold: (... 32 chars omitted)
                child 0, correlation_score: string
                child 1, hardware_note: string
                child 2, leakage_score: string
                child 3, promotion_threshold: string
                child 4, tvd_from_ideal: string
              timestamp: timestamp[s]
              version: string
              whitespace_analysis: struct<blue_ocean_positioning: struct<analogy: string, what_aevion_is: string, what_aevion_is_not: s (... 162 chars omitted)
                child 0, blue_ocean_positioning: struct<analogy: string, what_aevion_is: string, what_aevion_is_not: string, why_now: string>
                    child 0, analogy: string
                    child 1, what_aevion_is: string
                    child 2, what_aevion_is_not: string
                    child 3, why_now: string
                child 1, category: string
                child 2, novelty_claims: list<item: struct<backed_by: list<item: string>, claim: string, evidence: string, uniqueness: string (... 2 chars omitted)
                    child 0, item: struct<backed_by: list<item: string>, claim: string, evidence: string, uniqueness: string>
                        child 0, backed_by: list<item: string>
                            child 0, item: string
                        child 1, claim: string
                        child 2, evidence: string
                        child 3, uniqueness: string
              row_count_band: string
              parquet_mirror_ref: string
              hf_dataset_id: string
              schema_version: string
              parquet_mirror_note: string
              simulator_only: bool
              claim_ceiling: string
              hf_dataset_url: string
              public_claim_permitted: bool
              hardware_correctness_claim_permitted: bool
              license: string
              to
              {'schema_version': Value('string'), 'hf_dataset_id': Value('string'), 'hf_dataset_url': Value('string'), 'parquet_mirror_ref': Value('string'), 'parquet_mirror_note': Value('string'), 'row_count_band': Value('string'), 'license': Value('string'), 'simulator_only': Value('bool'), 'hardware_correctness_claim_permitted': Value('bool'), 'public_claim_permitted': Value('bool'), 'claim_ceiling': Value('string')}
              because column names don't match

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Quantum Governance Benchmark v0.1

Quantum Governance Benchmark — research benchmark seed.

All experiments in this release are simulator-first unless a row explicitly carries a hardware receipt with hardware_correctness_claim_permitted: false. Tags bell-state, ghz, and cross-vendor describe circuit types and comparative simulation rows, not certification that real QPU runs are production-grade.

Flag	Value
`simulator_only`	true (default for benchmark seed)
`hardware_correctness_claim_permitted`	false
`public_claim_permitted`	true
`CLAIM_CEILING`	`RESEARCH_BENCHMARK_GOVERNANCE_SIGNAL`

What this is

Proof-carrying quantum governance benchmark: schema-validated receipts, classical baselines, EGON-style overclaim checks, and explicit claim boundaries. This is a quantum honesty benchmark, not a quantum advantage benchmark.

What this does not prove

Quantum advantage or supremacy
Hardware correctness or cross-vendor hardware certification
Readiness for production-scale quantum jobs
Formal proof of quantum behavior in Mathlib/Lake sense

Hub notes (not repo truth by itself)

Hugging Face may auto-create a Parquet mirror at refs/convert/parquet after JSON validation. That indicates the dataset is well-formed on the Hub, not that Aevion proved quantum advantage.
Query examples (require datasets / network): see docs/research/hf_quantum_governance_benchmark_20260528.md in the source repository.

Reproducibility (source repo)

python3 -B -m pytest tests/benchmarks/test_hf_qgov_dataset_card_hygiene.py -v
python3 scripts/validate_hf_qgov_dataset_card.py

Publisher

Aevion LLC (SDVOSB, CAGE 15NV7) — research benchmark seed, not a production certification artifact.

Patent notice

Patent pending: proof-native compliance automation, subject to the scope and support of the filed application.

License

Apache-2.0

Downloads last month: 32

Total file size:

14.4 kB