Update README.md

README.md

@@ -79,6 +79,28 @@ Each sample in the Parquet files contains:
 ### 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: