Calibration-Conditioned FiLM Decoders for Low-Latency Decoding of Quantum Error Correction Evaluated on IBM Repetition-Code Experiments - Datasets

Raw experimental dataset accompanying the paper "Calibration-Conditioned FiLM Decoders for Low-Latency Decoding of Quantum Error Correction Evaluated on IBM Repetition-Code Experiments."

This dataset contains repetition-code experiments executed on three IBM Quantum processors -- ibm_kingston, ibm_pittsburgh, and ibm_fez. It comprises 352 hardware snapshots spanning code distances d = 3, 5, 7, 9, 11, syndrome-round counts r = 1 to 11, and both the X and Z logical bases. Each snapshot runs several repetition-code chains in parallel and carries the device calibration data captured at execution time, totalling several million measurement shots.

All data is provided raw, exactly as returned by the hardware -- no machine-learning processing or sparsification is applied -- and is anonymized (no IBM Runtime job identifiers are released).

CONTENTS

  - ibm_kingston.tar.gz, ibm_pittsburgh.tar.gz, ibm_fez.tar.gz
        per-device archives, each unpacking to <device>/d<D>_r<R>/job_<n>/
  - index.csv
        one row per snapshot: path, backend, d, rounds, basis, logical_states, n_chains, shots
  - README.md
        full description of the layout and field definitions

Each job_<n>/ directory contains:

  - info.json         experiment parameters (device, d, rounds, basis, states, shots, n_chains)
  - calibration.json  the device calibration snapshot at execution time (T1, T2, gate and readout error rates, coupling map)
  - circuit_state0.qasm, circuit_state1.qasm   transpiled circuits as executed
  - bitstrings.json   raw per-shot measurement records for every parallel chain

USAGE

Because each snapshot stores its own calibration data, the per-device and per-(d, r, basis) structure used in the paper is recoverable by filtering index.csv. The same calibration is consumed by both the FiLM decoder (via its calibration-graph encoder) and the modified MWPM baseline (via its detector-graph edge weights).

Github to be updated for corresponding experiments on approval. Please contact Samuel Stein (samuel.stein@pnnl.gov) if you need any information or source docs before hand.