---
license: cc-by-nc-4.0
task_categories:
- tabular-regression
- feature-extraction
language:
- en
tags:
- qiskit
- quantum-circuits
- synthetic-dataset
- benchmark
- expectation-values
- quantum-computing
- qml-benchmark
- quantum dataset
- qml dataset
- quantum benchmark
- noisy quantum data
- depolarizing noise
- error mitigation
- noise robustness

pretty_name: QSBench Depolarizing Demo v1.0.0 – Noisy Quantum Dataset (Depolarizing Noise, n=6)
size_categories:
- 1K<n<10K
---

![QSBench Logo](https://i.imgur.com/VyLgYtf.png)  
🌐 [Website](https://qsbench.github.io) | 🤗 [Dataset](https://huggingface.co/datasets/QSBench/QSBench-Depolarizing-v1.0.0-demo) | 🛠️ [GitHub](https://github.com/QSBench/QSBench-Depolarizing-v1.0.0-demo) | 🚀 [Interactive Demo](https://huggingface.co/QSBench/spaces)

# QSBench Depolarizing Demo v1.0.0

**Quantum Machine Learning dataset for noise robustness and error prediction.** Includes paired ideal and noisy expectation values under depolarizing noise.

Keywords: quantum dataset, noisy quantum circuits, depolarizing noise, QML benchmark, expectation value prediction.

**5000 synthetic quantum circuits with depolarizing noise** — demo subset of the QSBench Noise Pack.

Designed for researchers and engineers working on noise-aware quantum ML, robustness analysis, and error mitigation.

### Why this dataset?
Real quantum hardware is noisy. Depolarizing noise is one of the most widely studied noise models in quantum computing. This dataset allows you to:
- Compare **ideal vs noisy outputs**
- Train models that **predict or correct noise effects**
- Benchmark **robustness of ML models**
- Study **error distributions in quantum circuits**

### Use Cases
- Noise robustness benchmarking  
- Error mitigation research  
- Predicting noisy expectation values  
- Learning error correction models  
- Comparing clean vs noisy quantum outputs  

### Dataset Overview
- **Samples**: 5000  
- **Qubits**: 6  
- **Depth**: 4  
- **Circuit Families**: Mixed (HEA, RealAmplitudes, QFT, Efficient SU(2), Random)  
- **Entanglement**: Full  
- **Noise**: Depolarizing (p = 0.02–0.03 range)  
- **Observables**: Z, X, Y in mixed mode (global + per-qubit)  
- **Shots**: 512  
- **Splits**: Train / Validation / Test — deterministic hash-based  

### What's Inside Each Sample
Each sample in the Parquet files contains:
- Raw and transpiled QASM representations  
- Circuit adjacency matrix  
- Gate statistics (CX, H, RX, RY, RZ, etc.)  
- Structural metrics: Gate entropy + Meyer-Wallach entanglement  
- **Ideal expectation values**
- **Noisy expectation values** (after depolarizing noise)
- **Explicit error targets**: `error_<label> = ideal - noisy`
- Circuit metadata and generation parameters  
- Deterministic split label

### Key Learning Signals
For every observable, the dataset provides: `ideal_expval_*`, `noisy_expval_*`, `error_*`, `sign_ideal_*`, `sign_noisy_*`. This enables complex regression and classification tasks for noise modeling.

## QSBench-Depolarizing: Quantum Error Prediction

**You don't need a PhD in Quantum Physics to use this dataset.** Think of this as a classic **Predictive Maintenance** or **Regression** problem. We have a machine (the quantum computer) that makes errors. We give you the blueprints of the tasks it ran, and the magnitude of the errors it made. 

### The ML Mission: Supervised Regression

Your goal is to predict the `error` without running expensive physics simulations. Can you train a Gradient Boosting model (XGBoost/LightGBM) or a Neural Network to predict the output error based purely on the circuit's structural features?

### Dataset Anatomy (Features & Targets)

Use the structural features as `X`, and the errors as `y`.

| Group | Column Name | What is it for ML? |
| :--- | :--- | :--- |
| **Features (X)** | `depth`, `gate_entropy`, `cx_count` | The structural complexity of the task. |
| **Features (X)** | `noise_prob`, `shots` | The environmental conditions (error probability and sampling rate). |
| **Target (y)** | `error_Z_global` | **The Main Target.** The continuous error value you want to predict. |
| **Target (y)** | `sign_ideal_Z`, `sign_noisy_Z` | **For Classification.** Did the noise flip the final answer? (Binary target). |

### Quick Start Idea
Build a robust XGBoost regressor using `total_gates`, `depth`, and `noise_prob` to predict `error_Z_global`. What is your Mean Absolute Error (MAE)?

### Load the Dataset
The dataset is stored in Parquet format inside the `data/shards/` folder. You can load it directly using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

# Load the depolarizing demo dataset
dataset = load_dataset("QSBench/QSBench-Depolarizing-v1.0.0-demo", split="train")

# Inspect the first sample with noise data
print(dataset[0])
```

### Repository Structure

The dataset is stored in the `main` branch and contains only the data files to ensure the Dataset Viewer works correctly:

```text
QSBench-Depolarizing-v1.0.0-demo/
├── README.md # This file
└── data/ # Parquet shards (main data)
    └── shards/
        └── *.parquet
        └── *.csv
```

All metadata files (coverage.json, schema.json, meta.json, etc.) are located in a separate branch called `meta`.

👉 browse [meta branch](https://huggingface.co/datasets/QSBench/QSBench-Depolarizing-v1.0.0-demo/tree/metadata)

### Related QSBench Datasets

- [Core Pack (2k samples)](https://huggingface.co/datasets/QSBench/QSBench-Core-v1.0.0-demo)
- [Amplitude Damping Pack (5k samples)](https://huggingface.co/datasets/QSBench/QSBench-Amplitude-v1.0.0-demo)
- [Transpilation Hardware Pack (5k samples)](https://huggingface.co/datasets/QSBench/QSBench-Transpilation-v1.0.0-demo)
- [Thermal Relaxation Pack (2k samples)](https://huggingface.co/datasets/QSBench/QSBench-Thermal-Demo-v1.0.0)
- [Realistic hardware-mimic (2k samples)](https://huggingface.co/datasets/QSBench/QSBench-Device-Demo-v1.0.0)
- [Readout Error (2k samples)](https://huggingface.co/datasets/QSBench/QSBench-Readout-Demo-v1.0.0)

### Part of the QSBench Family

This is a small public **demo version**. Full-scale datasets (20k–150k+ samples), specialized noisy versions, and custom hardware packs are available.

[Repository](https://github.com/QSBench/QSBench-Depolarizing-v1.0.0-demo) | [Website & Full Catalog](https://qsbench.github.io)

**Email**: QSBench@gmail.com

### Notes

This dataset is fully synthetic and generated using quantum circuit simulation. No real-world or personal data is included.

**License:** CC BY-NC 4.0 (Personal & Research Use)

**Questions or custom requests?** Visit our [website](https://qsbench.github.io) or open an issue on [GitHub](https://github.com/QSBench/QSBench-Depolarizing-v1.0.0-demo).

### Support QSBench

You can support the project directly on this Giveth page:  
**[https://giveth.io/project/qsbench](https://giveth.io/project/qsbench)**

Your donations help us generate larger datasets, cover GPU costs, and continue developing new realistic noise models.

---
*Generated with QSBench Generator v5.0.2*