---
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
- amplitude damping
- error mitigation
- noise robustness

pretty_name: QSBench Amplitude Damping Demo v1.0.0 – Noisy Quantum Dataset (Amplitude Damping, n=8)
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-Amplitude-v1.0.0-demo) | 🛠️ [GitHub](https://github.com/QSBench/QSBench-Amplitude-v1.0.0-demo) | 🚀 [Interactive Demo](https://huggingface.co/QSBench/spaces)

# QSBench Amplitude Damping Demo v1.0.0

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

Keywords: quantum dataset, noisy quantum circuits, amplitude damping, relaxation noise, QML benchmark, expectation value prediction.

**5000 high-quality synthetic quantum circuits with amplitude damping** — demo subset of the QSBench Noise Pack.

Designed for researchers and engineers working on noise-aware quantum ML, energy relaxation analysis ($T_1$ effects), and error mitigation.

### Why this dataset?
Real quantum hardware suffers from energy relaxation. Amplitude damping is the standard model for describing the transition from an excited state to the ground state. This dataset allows you to:
- Compare **ideal vs noisy outputs** under relaxation effects
- Train models that **predict or correct decay-related errors**
- Benchmark **robustness of ML models** on 8-qubit circuits
- Study **error distributions** in deeper quantum circuits (depth=6)

### Use Cases
- Noise robustness benchmarking  
- Error mitigation research (specifically for $T_1$ relaxation)
- Predicting noisy expectation values  
- Learning error correction models  
- Feature engineering for noisy quantum states

### Dataset Overview
- **Samples**: 5000  
- **Qubits**: 8  
- **Depth**: 6  
- **Circuit Families**: Mixed (HEA, RealAmplitudes, QFT, Efficient SU(2), Random)  
- **Entanglement**: Full  
- **Noise**: Amplitude Damping ($p = 0.012$)  
- **Observables**: Z, X, Y in mixed mode (global + per-qubit)  
- **Shots**: 1024  
- **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 amplitude damping)
- **Explicit error targets**: `error_<label> = ideal - noisy`
- Circuit metadata and generation parameters (depth=6, n=8)
- Deterministic split label

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

### QSBench-Amplitude: Asymmetric Noise Prediction

**You don't need a PhD in Quantum Physics to use this dataset.** This dataset represents a specific type of hardware degradation (Amplitude Damping), similar to battery drain or signal loss over time. 

### The ML Mission: Complex Tabular Regression
Unlike simple symmetric noise, Amplitude Damping affects the system in a biased way. Your mission is to build a predictive model that understands how specific topological structures (e.g., dense adjacency graphs) exacerbate this specific type of signal loss.

### Dataset Anatomy (Features & Targets)

| Group | Column Name | What is it for ML? |
| :--- | :--- | :--- |
| **Features (X)** | `adjacency` | The graph structure. Dense graphs decay differently than sparse ones. |
| **Features (X)** | `qasm_transpiled` | The hardware-specific compiled code (NLP feature). |
| **Features (X)** | `single_qubit_gates`, `two_qubit_gates` | The operation counts. |
| **Target (y)** | `error_Z_global`, `error_X_global` | Continuous regression targets representing the signal loss. |
| **Physics** | `meyer_wallach` | A measure of entanglement. Does highly entangled data decay faster? |

### Quick Start Idea
Compare feature importances. Does `depth` matter more than the number of `two_qubit_gates` when predicting Amplitude Damping errors?

### 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 amplitude damping demo dataset
dataset = load_dataset("QSBench/QSBench-Amplitude-v1.0.0-demo", split="train")

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

If you prefer to use `pandas`:

```python
import pandas as pd

# Load all Parquet shards from the data folder
df = pd.read_parquet("data/shards/*.parquet")
print(df[["ideal_expval_Z_global", "noisy_expval_Z_global", "error_Z_global"]].head())
```

### 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-Amplitude-v1.0.0-demo/
├── README.md # This file
└── data/ # Parquet and CSV shards
    └── 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-Amplitude-v1.0.0-demo/tree/metadata)

### Related QSBench Datasets

- [Core Pack (2k samples)](https://huggingface.co/datasets/QSBench/QSBench-Core-v1.0.0-demo)
- [Depolarizing Pack (5k samples)](https://huggingface.co/datasets/QSBench/QSBench-Depolarizing-Demo-v1.0.0)
- [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.

[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-Amplitude-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*