init

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+from datasets import load_dataset
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import r2_score
+import matplotlib.pyplot as plt
+
+# Загрузка датасета
+ds = load_dataset("QSBench/QSBench-Core-v1.0.0-demo")
+
+# Функция для отображения данных выбранного сплита
+def show_data(split):
+    df = pd.DataFrame(ds[split])
+    return df.head(10)
+
+# Функция для обучения модели
+def train_and_plot():
+    feature_cols = ["total_gates", "gate_entropy", "meyer_wallach"]
+    target_col = "ideal_expval_Z_global"
+    X_train = pd.DataFrame(ds["train"])[feature_cols]
+    y_train = pd.DataFrame(ds["train"])[target_col]
+    X_test = pd.DataFrame(ds["test"])[feature_cols]
+    y_test = pd.DataFrame(ds["test"])[target_col]
+
+    model = RandomForestRegressor(n_estimators=100, random_state=42)
+    model.fit(X_train, y_train)
+    y_pred = model.predict(X_test)
+    r2 = r2_score(y_test, y_pred)
+
+    fig, ax = plt.subplots()
+    ax.scatter(y_test, y_pred, alpha=0.5)
+    ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--')
+    ax.set_xlabel("True value")
+    ax.set_ylabel("Predicted")
+    ax.set_title(f"Predictions vs. Truth (R² = {r2:.4f})")
+    return fig
+
+with gr.Blocks(title="QSBench Demo Explorer") as demo:
+    gr.Markdown("""
+    # QSBench Core Demo Explorer
+    Interactive demo of the **QSBench Core Demo** dataset – 200 synthetic quantum circuits (6 qubits, depth 4).
+    This space shows how to load the data, inspect it, and train a simple model on the ideal expectation values.
+
+    👉 **Full datasets (up to 200k samples, noisy versions, 10‑qubit transpilation packs) are available for purchase.**  
+    [Visit the QSBench website](https://qsbench.github.io/)
+    """)
+    with gr.Tabs():
+        with gr.TabItem("Data Explorer"):
+            split_selector = gr.Dropdown(choices=["train", "validation", "test"], label="Choose a split", value="train")
+            data_table = gr.Dataframe()
+            split_selector.change(fn=show_data, inputs=split_selector, outputs=data_table)
+        with gr.TabItem("Model Demo"):
+            train_button = gr.Button("Train Random Forest")
+            plot_output = gr.Plot()
+            train_button.click(fn=train_and_plot, outputs=plot_output)
+    gr.Markdown("---")
+    gr.Markdown("### Get the full datasets\n- **QSBench Core** – 75k clean circuits (8 qubits)\n- **Depolarizing Noise Pack** – 150k circuits with depolarizing noise\n- **Amplitude Damping Pack** – 150k circuits with T1‑like relaxation\n- **Transpilation Hardware Pack** – 200k circuits (10 qubits) with hardware‑aware transpilation\n\n🔗 [Browse all datasets and purchase licenses](https://qsbench.github.io/)")
+
+demo.launch()

requirements.txt

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+streamlit
+datasets
+pandas
+scikit-learn
+matplotlib