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ibm-qml-kernel

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quantum-computing
ibm-quantum
qiskit
superconducting-qubits
reproducibility
benchmarks
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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 3 was different: 
backend_name: string
timestamp: string
num_qubits: int64
version: string
basis_gates: list<item: string>
properties_timestamp: timestamp[s]
vs
backend: string
backend_type: string
noise_source: string
experiment_mode: string
measurement_basis: string
circuit_mode: string
mu: int64
shots: int64
counts: struct<1110: int64, 1011: int64, 0010: int64, 1001: int64, 1000: int64, 0100: int64, 1100: int64, 1010: int64, 0110: int64, 0011: int64, 0001: int64, 0111: int64, 1101: int64, 1111: int64, 0101: int64, 0000: int64>
W_X: double
W_X_error: double
W_X_ideal: double
W_X_tilde: double
W_X_tilde_error: double
parity_counts: struct<n_even: int64, n_odd: int64, p_even: double, p_odd: double>
circuit_stats: struct<num_qubits: int64, num_clbits: int64, depth: int64, size: int64, gate_counts: struct<h: int64, cx: int64, x: int64>, two_qubit_gate_count: int64>
transpiled_depth: int64
transpiled_size: int64
optimization_level: int64
noise_params: struct<T1_us: int64, T2_us: int64, single_qubit_error_pct: double, two_qubit_error_pct: double, readout_error_pct: double>
timestamp: string
qiskit_version: struct<qiskit: string, qiskit_aer: string>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 531, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 3 was different: 
              backend_name: string
              timestamp: string
              num_qubits: int64
              version: string
              basis_gates: list<item: string>
              properties_timestamp: timestamp[s]
              vs
              backend: string
              backend_type: string
              noise_source: string
              experiment_mode: string
              measurement_basis: string
              circuit_mode: string
              mu: int64
              shots: int64
              counts: struct<1110: int64, 1011: int64, 0010: int64, 1001: int64, 1000: int64, 0100: int64, 1100: int64, 1010: int64, 0110: int64, 0011: int64, 0001: int64, 0111: int64, 1101: int64, 1111: int64, 0101: int64, 0000: int64>
              W_X: double
              W_X_error: double
              W_X_ideal: double
              W_X_tilde: double
              W_X_tilde_error: double
              parity_counts: struct<n_even: int64, n_odd: int64, p_even: double, p_odd: double>
              circuit_stats: struct<num_qubits: int64, num_clbits: int64, depth: int64, size: int64, gate_counts: struct<h: int64, cx: int64, x: int64>, two_qubit_gate_count: int64>
              transpiled_depth: int64
              transpiled_size: int64
              optimization_level: int64
              noise_params: struct<T1_us: int64, T2_us: int64, single_qubit_error_pct: double, two_qubit_error_pct: double, readout_error_pct: double>
              timestamp: string
              qiskit_version: struct<qiskit: string, qiskit_aer: string>

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IBM-QML-Kernel Branch-Transfer Benchmarks (ibm-qml-kernel)

Dataset Summary

This dataset is the reproducibility artifact bundle corresponding to the arXiv submission:

“Wigner's Friend as a Circuit: Inter-Branch Communication Witness Benchmarks on Superconducting Quantum Hardware.”

It snapshots the experimental and simulation outputs for a five‑qubit “branch‑transfer / message‑transfer” circuit primitive (message transfer in the compiled circuit / measurement record sense, not physical signaling), executed on IBM Quantum hardware and mirrored with backend‑matched noisy simulations.

What’s inside (high level)

The release is designed as a “stable, citeable checkpoint” and includes, at minimum:

  • Hardware execution on IBM Quantum ibm_fez (N = 20,000 shots).
  • Coherence-sensitive witness evaluation (X and Y bases) + a visibility baseline.
  • Backend-matched noisy simulations using calibration-synchronized noise models.
  • Execution provenance: IBM Quantum job IDs + backend calibration snapshots.
  • Deterministic figure regeneration from archived artifacts.
  • Tamper-evident manifest: SHA256 hashes for bundle files.

(See the GitHub release notes for the canonical inventory.)

Intended Use

This dataset is for:

  • Reproducing figures/tables/values from the associated paper.
  • Auditing compilation + noise impacts on the reported witnesses.
  • Serving as a reference artifact for future “branch-transfer / inter-branch witness” benchmark runs on other devices/backends.

Not intended for: training NLP/Vision models. It’s an experiment + provenance bundle.

How to Use

Option A — download the full artifact snapshot (recommended for exact reproduction)

For non-tabular artifacts (plots, calibration dumps, intermediate files), the most faithful workflow is to download the full repository snapshot:

from huggingface_hub import snapshot_download

local_dir = snapshot_download(
    repo_id="Cohaerence/wigner-friend-v2b",
    repo_type="dataset",
)
print(local_dir)

Option B — datasets.load_dataset(...) (best if files are extracted + structured)

Hugging Face Datasets works best when the dataset includes common formats like csv, jsonl, or parquet, optionally referenced via data_files=.

from datasets import load_dataset

ds = load_dataset("Cohaerence/wigner-friend-v2b")
print(ds)

Docs: https://huggingface.co/docs/datasets/loading

Reproduction Quickstart (paper-aligned)

These commands reflect the intended “paper-aligned” reproduction workflow described in the release notes:

# Verify IBM Quantum connectivity
python -c "from qiskit_ibm_runtime import QiskitRuntimeService as S; s=S(); bs=s.backends(simulator=False, operational=True); print('n_backends=', len(bs))"

# Hardware coherence witness (X + Y bases)
python -m experiments.branch_transfer.run_ibm   --backend ibm_fez --mode coherence_witness   --include-y-basis --shots 20000 --optimization-level 2

# Hardware visibility (rp_z mode)
python -m experiments.branch_transfer.run_ibm   --backend ibm_fez --mode rp_z --mu 1   --shots 20000 --optimization-level 2

# Backend-matched noisy simulations
python -m experiments.branch_transfer.run_sim   --mode coherence_witness --include-y-basis --mu 1   --shots 20000 --noise-from-backend ibm_fez
python -m experiments.branch_transfer.run_sim   --mode rp_z --mu 1 --shots 20000   --noise-from-backend ibm_fez

# Generate analysis figures
python -m experiments.branch_transfer.analyze   --artifacts-dir artifacts/branch_transfer   --figures-dir artifacts/branch_transfer/figures --plot-all

Citations

References

  1. Violaris, M. (2026). Quantum observers can communicate across multiverse branches. arXiv:2601.08102. arXiv:2601.08102

  2. Mukherjee, S. and Hance, J. Limits of absoluteness of observed events in timelike scenarios: A no-go theorem, arXiv:2510.26562. arXiv:2510.26562

License

MIT License. See LICENSE for details.

Contact

Christopher Altman (2026)

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