README.md

metadata

language:
  - en
license: mit
size_categories:
  - n<1K
task_categories:
  - other
tags:
  - quantum-computing
  - quantum-noise
  - error-mitigation
  - NISQ
  - IBM-Quantum
  - transfer-learning
  - few-shot-learning
  - physics
pretty_name: 'Quantum Noise Transfer: Cross-Device Few-Shot Adaptation'
dataset_info:
  features:
    - name: circuit_id
      dtype: string
    - name: backend
      dtype: string
    - name: circuit_type
      dtype: string
    - name: n_qubits
      dtype: int32
    - name: T1_mean
      dtype: float64
    - name: T2_mean
      dtype: float64
    - name: readout_error_mean
      dtype: float64
    - name: cx_error_mean
      dtype: float64
    - name: noisy_distribution
      dtype: string
    - name: ideal_distribution
      dtype: string
    - name: x
      sequence: float64
    - name: 'y'
      sequence: float64
  splits:
    - name: train
      num_examples: 170
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-00000-of-00001.parquet

Quantum Noise Transfer: Cross-Device Few-Shot Adaptation Dataset

Paper: Few-Shot Cross-Device Transfer for Quantum Noise Modeling on Real Hardware

Authors: Sahil Al Farib, Sheikh Redwanul Islam, Azizur Rahman Anik

Dataset Description

A real-hardware quantum noise dataset collected from two IBM Quantum devices for studying cross-device transfer learning in quantum error mitigation. Each sample pairs a noisy output distribution (measured on real hardware) with the corresponding ideal output distribution (from noiseless simulation), augmented with device calibration features.

Source Devices

Device	Role	Samples
ibm_fez	Source (Backend A)	85
ibm_marrakesh	Target (Backend B)	85
Total		170

Circuit Composition

Circuit Type	Count	Purpose
Random	40	Structural diversity; generalization
Bell state	15	Two-qubit entanglement; CX error sensitivity
GHZ state	15	Multi-qubit entanglement; error accumulation
QFT	15	Layered gate accumulation; coherent errors

All circuits use 2-5 qubits, depth 2-8, and 8,192 shots per execution.

Device Calibration Comparison

Property	ibm_fez (A)	ibm_marrakesh (B)	Delta
T1 (us)	142.4	192.8	+35.4%
T2 (us)	104.1	114.0	+9.6%
Readout error	0.0285	0.0335	+17.5%
CX gate error	0.0328	0.0560	+70.7%

Dataset Fields

Field	Type	Description
circuit_id	string	Unique circuit identifier (UUID)
backend	string	IBM Quantum backend name (ibm_fez or ibm_marrakesh)
circuit_type	string	Circuit family: random, bell, ghz, or qft
n_qubits	int	Number of qubits (2-5)
T1_mean	float	Mean qubit relaxation time (seconds)
T2_mean	float	Mean qubit dephasing time (seconds)
readout_error_mean	float	Mean readout error rate
cx_error_mean	float	Mean CX (CNOT) gate error rate
noisy_distribution	string (JSON)	Measured probability distribution from real hardware
ideal_distribution	string (JSON)	Ground-truth distribution from noiseless simulation
x	list[float]	41-dim input feature vector (circuit + calibration + noisy dist)
y	list[float]	32-dim target vector (ideal distribution, zero-padded)

Feature Vector (x) Layout (41 dimensions)

Index	Feature
0	Number of qubits
1	Circuit depth
2	CX gate count
3	H gate count
4	X gate count
5	Mean T1 (standardized)
6	Mean T2 (standardized)
7	Mean readout error (standardized)
8	Mean CX gate error (standardized)
9-40	Noisy output distribution (32-dim, zero-padded)

Additional Files

figures/              # All paper figures (PNG + PDF)
data/raw/             # Raw data from IBM Quantum hardware
  calibration_A.json  # ibm_fez calibration snapshot
  calibration_B.json  # ibm_marrakesh calibration snapshot
  circuit_meta.json   # Circuit structure metadata
  ideal.json          # Ideal (simulated) distributions
  noisy_A.json        # Noisy measurements from ibm_fez
  noisy_B.json        # Noisy measurements from ibm_marrakesh
data/processed/       # Preprocessed datasets
  dataset.json        # Full dataset (JSON format)
  dataset_standardized.json  # Standardized features
results/              # Experiment results
  experiment_results_fixed.json
  ablation_results_fixed.json
  training_history.json
  example_prediction.json

Usage

from datasets import load_dataset

ds = load_dataset("sahilfarib/quantum-noise-transfer")

# Filter by backend
source = ds["train"].filter(lambda x: x["backend"] == "ibm_fez")
target = ds["train"].filter(lambda x: x["backend"] == "ibm_marrakesh")

print(f"Source samples: {len(source)}")  # 85
print(f"Target samples: {len(target)}")  # 85

Key Results

Using this dataset, a Residual Noise Adapter trained on ibm_fez achieves:

Condition	KL Divergence	Improvement
In-domain (A->A)	0.3014	--
Zero-shot (A->B)	1.6706	baseline
Few-shot K=20	1.1924	-28.6%

Citation

@article{farib2026fewshot,
  title={Few-Shot Cross-Device Transfer for Quantum Noise Modeling on Real Hardware},
  author={Farib, Sahil Al and Islam, Sheikh Redwanul and Anik, Azizur Rahman},
  journal={arXiv preprint arXiv:2604.24397},
  year={2026}
}

License

MIT