Update README.md

README.md

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-license: cc-by-nc-4.0
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+---
+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
+- device noise
+- hardware-mimic
+- thermal relaxation
+- error mitigation
+- noise robustness
+pretty_name: QSBench Device Demo v1.0.0 – Realistic Device-like Noise (GenericBackendV2, n=10)
+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-Device-Demo-v1.0.0) | 🛠️ [GitHub](https://github.com/QSBench/QSBench-Device-Demo-v1.0.0) | 🚀 [Interactive Demo](https://huggingface.co/QSBench/spaces)
+
+# QSBench Device Demo v1.0.0
+
+**Realistic hardware-mimic quantum dataset** — the most physically accurate noise demo in the QSBench family.
+
+This release uses `device` noise model based on `GenericBackendV2`, which simulates a full set of realistic hardware errors (T1/T2 relaxation, gate errors, readout errors, and crosstalk-like effects).
+
+**2048 high-quality synthetic quantum circuits with realistic device-like noise.**
+
+Designed for researchers working on **sim-to-real transfer**, hardware-aware quantum ML, and benchmarking models under conditions closest to real quantum processors.
+
+### Why this dataset?
+Most synthetic datasets use simplified noise (depolarizing or amplitude damping).  
+`Device` noise is much closer to what you see on actual IBM, Rigetti or IonQ hardware.  
+This dataset helps close the **sim-to-real gap**.
+
+### Use Cases
+- Sim-to-real transfer learning
+- Hardware-aware model benchmarking
+- Testing robustness under realistic multi-source noise
+- Error mitigation research (including crosstalk approximation)
+- Comparing simplified noise vs real-device noise
+
+### Dataset Overview
+- **Samples**: 2048
+- **Qubits**: 10
+- **Depth**: 8
+- **Circuit Families**: Mixed (HEA, RealAmplitudes, QFT, Efficient SU(2), Random)
+- **Entanglement**: Full
+- **Noise**: `device` (GenericBackendV2 — realistic device-like noise)
+- **Observables**: Z, X, Y in mixed mode (global + per-qubit)
+- **Shots**: 1024
+- **Splits**: Train / Validation / Test — deterministic hash-based
+
+### What's Inside Each Sample
+- Raw and transpiled QASM
+- Circuit adjacency matrix
+- Detailed gate statistics
+- Structural metrics (gate entropy, Meyer-Wallach entanglement)
+- **Ideal expectation values**
+- **Noisy expectation values** (after realistic device noise)
+- **Error targets**: `error_<label>`
+- Full generation metadata
+
+### Key Advantage
+Unlike single-channel noise models, `device` noise combines multiple realistic error sources simultaneously — exactly what happens on real quantum hardware.
+
+### Load the Dataset
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("QSBench/QSBench-Device-Demo-v1.0.0", split="train")
+print(dataset[0])
+```
+
+Using pandas:
+
+```python
+import pandas as pd
+df = pd.read_parquet("data/shards/*.parquet")
+print(df[["ideal_expval_Z_global", "noisy_expval_Z_global", "error_Z_global"]].head())
+```
+
+### Repository Structure
+
+Data is stored in the `main` branch:
+
+```text
+QSBench-Device-Demo-v1.0.0/
+├── README.md
+└── data/
+    └── shards/
+        └── *.parquet
+```
+
+Metadata files are available in the `metadata` branch.
+
+### Related QSBench Datasets
+
+- [QSBench-Thermal-Demo-v1.0.0](https://huggingface.co/datasets/QSBench/QSBench-Thermal-Demo-v1.0.0)
+
+- [QSBench-Amplitude-v1.0.0-demo](https://huggingface.co/datasets/QSBench/QSBench-Amplitude-v1.0.0-demo)
+
+- [QSBench-Depolarizing-Demo-v1.0.0](https://huggingface.co/datasets/QSBench/QSBench-Depolarizing-Demo-v1.0.0)
+
+- [QSBench-Core-v1.0.0-demo](https://huggingface.co/datasets/QSBench/QSBench-Core-v1.0.0-demo)
+
+### Part of the QSBench Family
+
+This is a public demo version. Full-scale Device Noise Pack and other specialized releases are available via the [QSBench Generator](https://github.com/QSBench/QSBench-Generator).
+
+### Notes
+
+- Fully synthetic, generated with Qiskit Aer + GenericBackendV2
+
+- **License:** CC BY-NC 4.0 (Personal & Research Use)
+
+**Questions or custom requests?** Visit [qsbench.github.io](https://qsbench.github.io/) or open an [issue on GitHub](https://github.com/QSBench/QSBench-Device-Demo-v1.0.0).
+
+---
+*Generated with QSBench Generator v5.1.0*