QSBench/QSBench-Thermal-Demo-v1.0.0

---
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
- thermal relaxation
- T1 T2 noise
- error mitigation
- noise robustness
pretty_name: QSBench Thermal Relaxation Demo v1.0.0 – Noisy Quantum Dataset (T1/T2 Thermal Noise, n=8)
size_categories:
- 1K<n<10K
---
🌐 [Website](https://qsbench.github.io) | 🤗 [Dataset](https://huggingface.co/datasets/QSBench/QSBench-Thermal-Demo-v1.0.0) | 🛠️ [GitHub](https://github.com/QSBench/QSBench-Thermal-Demo-v1.0.0) | 🚀 [Interactive Demo](https://huggingface.co/QSBench/spaces)

# QSBench Thermal Relaxation Demo v1.0.0

**Quantum Machine Learning dataset for realistic noise robustness and sim-to-real research.**
Includes paired ideal and noisy expectation values under **thermal relaxation** (T1/T2) noise — the most physically relevant noise model on current quantum hardware.

**2048 high-quality synthetic quantum circuits with thermal relaxation noise** — demo subset of the QSBench Noise Pack.

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

### Why this dataset?
Real quantum hardware is dominated by **energy relaxation (T1)** and **dephasing (T2)**. Thermal relaxation is the standard model used by IBM, Google, IonQ and most superconducting platforms.
This dataset allows you to:
- Study realistic **T1/T2 decoherence** effects
- Train models that predict or mitigate relaxation-induced errors
- Benchmark **robustness** under physically accurate noise
- Explore sim-to-real transfer on 8-qubit circuits

### Use Cases
- Noise robustness benchmarking (T1/T2)
- Error mitigation research (especially relaxation errors)
- Predicting noisy expectation values
- Learning error correction models
- Feature engineering for decoherence-aware quantum ML

### Dataset Overview
- **Samples**: 2048
- **Qubits**: 8
- **Depth**: 6
- **Circuit Families**: Mixed (HEA, RealAmplitudes, QFT, Efficient SU(2), Random)
- **Entanglement**: Full
- **Noise**: Thermal Relaxation (`T1 = 50 μs`, `T2 = 30 μs`)
- **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 thermal relaxation)
- **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 supports both high-precision regression and classification tasks for realistic noise modeling.

### QSBench-Thermal: Realistic Decoherence Modeling
**You don't need a PhD in Quantum Physics to use this dataset.**
This dataset represents the dominant noise channel on today's quantum processors — thermal relaxation. It behaves like gradual "battery drain" of quantum information over time.

### The ML Mission: Complex Tabular Regression
Unlike symmetric depolarizing noise, thermal relaxation is **biased** and time-dependent. Your mission is to build a predictive model that understands how circuit structure (adjacency, gate counts, entanglement) influences T1/T2 decay rates.

### Dataset Anatomy (Features & Targets)

| Group | Column Name | What is it for ML? |
|----------------|------------------------------|--------------------|
| **Features (X)** | `adjacency` | Graph structure — dense graphs lose coherence faster |
| **Features (X)** | `qasm_transpiled` | Hardware-specific compiled circuit (NLP feature) |
| **Features (X)** | `single_qubit_gates`, `two_qubit_gates` | Operation counts |
| **Target (y)** | `error_Z_global`, `error_X_global` | Continuous regression targets (relaxation loss) |
| **Physics** | `meyer_wallach` | Entanglement measure — does high entanglement decay faster? |

### Quick Start Idea
Compare feature importances. Does `meyer_wallach` or `two_qubit_gates` matter more when predicting thermal relaxation errors?

### Load the Dataset
```python
from datasets import load_dataset

# Load the thermal relaxation demo dataset
dataset = load_dataset("QSBench/QSBench-Thermal-Demo-v1.0.0", split="train")

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

### Repository Structure

The dataset is stored in the `main` branch and contains only the data files:

```text
QSBench-Thermal-Demo-v1.0.0/
├── README.md
└── data/
└── shards/
└── *.parquet
└── *.csv
```

All metadata files (`meta.json`, `schema.json`, `coverage.json`, etc.) are in the `metadata` branch.
👉 browse [metadata branch](https://huggingface.co/datasets/QSBench/QSBench-Thermal-Demo-v1.0.0/tree/metadata)

### Related QSBench Datasets

- [QSBench-Core-v1.0.0-demo](https://huggingface.co/datasets/QSBench/QSBench-Core-v1.0.0-demo)

- [QSBench-Depolarizing-Demo-v1.0.0](https://huggingface.co/datasets/QSBench/QSBench-Depolarizing-Demo-v1.0.0)

- [QSBench-Amplitude-v1.0.0-demo](https://huggingface.co/datasets/QSBench/QSBench-Amplitude-v1.0.0-demo)

- [QSBench-Transpilation-v1.0.0-demo](https://huggingface.co/datasets/QSBench/QSBench-Transpilation-v1.0.0-demo)

### 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 on the [QSBench website](https://qsbench.github.io/).

### Notes

- Fully synthetic dataset generated with Qiskit Aer

- No real-world or personal data

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

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

### 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.

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*Generated with QSBench Generator v5.1.0*