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| import gradio as gr | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import pandas as pd | |
| import seaborn as sns | |
| import logging | |
| import requests | |
| from typing import List, Tuple, Dict, Optional | |
| from datasets import load_dataset | |
| from sklearn.ensemble import RandomForestRegressor | |
| from sklearn.metrics import mean_absolute_error, r2_score | |
| from sklearn.model_selection import train_test_split | |
| # --- CONFIG & LOGGING --- | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| REPO_CONFIG = { | |
| "Core (Clean)": { | |
| "repo": "QSBench/QSBench-Core-v1.0.0-demo", | |
| "meta_url": "https://huggingface.co/datasets/QSBench/QSBench-Core-v1.0.0-demo/raw/metadata/meta/meta.json", | |
| "report_url": "https://huggingface.co/datasets/QSBench/QSBench-Core-v1.0.0-demo/raw/metadata/meta/report.json" | |
| }, | |
| "Depolarizing Noise": { | |
| "repo": "QSBench/QSBench-Depolarizing-Demo-v1.0.0", | |
| "meta_url": "https://huggingface.co/datasets/QSBench/QSBench-Depolarizing-Demo-v1.0.0/raw/meta/meta/meta.json", | |
| "report_url": "https://huggingface.co/datasets/QSBench/QSBench-Depolarizing-Demo-v1.0.0/raw/meta/meta/report.json" | |
| }, | |
| "Amplitude Damping": { | |
| "repo": "QSBench/QSBench-Amplitude-v1.0.0-demo", | |
| "meta_url": "https://huggingface.co/datasets/QSBench/QSBench-Amplitude-v1.0.0-demo/raw/meta/meta/meta.json", | |
| "report_url": "https://huggingface.co/datasets/QSBench/QSBench-Amplitude-v1.0.0-demo/raw/meta/meta/report.json" | |
| }, | |
| "Transpilation (10q)": { | |
| "repo": "QSBench/QSBench-Transpilation-v1.0.0-demo", | |
| "meta_url": "https://huggingface.co/datasets/QSBench/QSBench-Transpilation-v1.0.0-demo/raw/meta/meta/meta.json", | |
| "report_url": "https://huggingface.co/datasets/QSBench/QSBench-Transpilation-v1.0.0-demo/raw/meta/meta/report.json" | |
| } | |
| } | |
| # Columns that are NOT features (system, categorical, or targets) | |
| NON_FEATURE_COLS = { | |
| "sample_id", "sample_seed", "circuit_hash", "split", "circuit_qasm", | |
| "qasm_raw", "qasm_transpiled", "circuit_type_resolved", "circuit_type_requested", | |
| "noise_type", "noise_prob", "observable_bases", "observable_mode", "backend_device", | |
| "precision_mode", "circuit_signature", "entanglement", "shots", "gpu_requested", "gpu_available" | |
| } | |
| _ASSET_CACHE = {} | |
| def load_all_assets(key: str) -> Dict: | |
| if key not in _ASSET_CACHE: | |
| logger.info(f"Fetching {key}...") | |
| ds = load_dataset(REPO_CONFIG[key]["repo"]) | |
| meta = requests.get(REPO_CONFIG[key]["meta_url"]).json() | |
| report = requests.get(REPO_CONFIG[key]["report_url"]).json() | |
| _ASSET_CACHE[key] = {"df": pd.DataFrame(ds["train"]), "meta": meta, "report": report} | |
| return _ASSET_CACHE[key] | |
| # --- UI LOGIC --- | |
| def get_methodology_content(ds_name: str): | |
| assets = load_all_assets(ds_name) | |
| meta, report = assets["meta"], assets["report"] | |
| params = meta.get("parameters", {}) | |
| families = report.get("families", {}) | |
| fam_table = "| Family | Samples | Description |\n|:---|:---|:---|\n" | |
| for f, count in families.items(): | |
| fam_table += f"| {f.upper()} | {count} | Synthetic {f} circuits |\n" | |
| return f""" | |
| ## 📖 Methodology: {meta.get('dataset_version')} | |
| **Generator:** QSBench v{meta.get('generator_version')} | |
| **Config:** {params.get('n_qubits')} Qubits | Depth {params.get('depth')} | Noise `{params.get('noise')}` (p={params.get('noise_prob')}) | |
| ### Circuit Family Coverage | |
| {fam_table} | |
| """ | |
| def sync_ml_metrics(ds_name: str): | |
| """Dynamically finds all available numerical metrics (features) from CSV/Dataset""" | |
| assets = load_all_assets(ds_name) | |
| df = assets["df"] | |
| # Extract all numeric columns | |
| numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist() | |
| # Filter: remove system IDs and targets (anything starting with ideal/noisy/error/sign) | |
| valid_features = [ | |
| c for c in numeric_cols | |
| if c not in NON_FEATURE_COLS | |
| and not any(prefix in c for prefix in ["ideal_", "noisy_", "error_", "sign_"]) | |
| ] | |
| # Priority metrics for "default" selection | |
| top_tier = ["gate_entropy", "meyer_wallach", "adjacency", "depth", "total_gates", "cx_count"] | |
| defaults = [f for f in top_tier if f in valid_features] | |
| return gr.update(choices=valid_features, value=defaults or valid_features[:5]) | |
| def train_model(ds_name: str, features: List[str]): | |
| if not features: return None, "### ❌ Error: No metrics selected." | |
| assets = load_all_assets(ds_name) | |
| df = assets["df"] | |
| # Use global Z value as target | |
| target = "ideal_expval_Z_global" | |
| train_df = df.dropna(subset=features + [target]) | |
| X, y = train_df[features], train_df[target] | |
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) | |
| model = RandomForestRegressor(n_estimators=100, max_depth=10, n_jobs=-1).fit(X_train, y_train) | |
| preds = model.predict(X_test) | |
| sns.set_theme(style="whitegrid", context="talk") | |
| fig, axes = plt.subplots(1, 3, figsize=(24, 8)) | |
| # 1. Prediction vs Reality | |
| axes[0].scatter(y_test, preds, alpha=0.3, color='#2c3e50') | |
| axes[0].plot([y.min(), y.max()], [y.min(), y.max()], 'r--', lw=2) | |
| axes[0].set_title(f"Accuracy (R²: {r2_score(y_test, preds):.3f})") | |
| axes[0].set_xlabel("Ideal ExpVal"); axes[0].set_ylabel("Predicted") | |
| # 2. Feature Importance | |
| imp = model.feature_importances_ | |
| # Take top 10 if there are many, or all if few | |
| top_n = min(len(features), 10) | |
| idx = np.argsort(imp)[-top_n:] | |
| axes[1].barh([features[i] for i in idx], imp[idx], color='#27ae60') | |
| axes[1].set_title(f"Top {top_n} Metrics Importance") | |
| # 3. Residuals | |
| sns.histplot(y_test - preds, kde=True, ax=axes[2], color='#d35400') | |
| axes[2].set_title("Residuals (Error Distribution)") | |
| plt.tight_layout(pad=3.0) | |
| return fig, f"**Mean Absolute Error (MAE):** {mean_absolute_error(y_test, preds):.4f}" | |
| def update_explorer(ds_name: str, split_name: str): | |
| assets = load_all_assets(ds_name) | |
| df = assets["df"] | |
| splits = df["split"].unique().tolist() if "split" in df.columns else ["train"] | |
| display_df = df[df["split"] == split_name].head(10) if "split" in df.columns else df.head(10) | |
| raw = display_df["qasm_raw"].iloc[0] if "qasm_raw" in display_df.columns else "// N/A" | |
| tr = display_df["qasm_transpiled"].iloc[0] if "qasm_transpiled" in display_df.columns else "// N/A" | |
| return gr.update(choices=splits), display_df, raw, tr, f"### 📋 {ds_name} Explorer" | |
| # --- INTERFACE --- | |
| with gr.Blocks(theme=gr.themes.Soft(), title="QSBench Hub") as demo: | |
| gr.Markdown("# 🌌 QSBench: Quantum Analytics Hub") | |
| with gr.Tabs(): | |
| with gr.TabItem("🔎 Explorer"): | |
| meta_txt = gr.Markdown("### Loading...") | |
| with gr.Row(): | |
| ds_sel = gr.Dropdown(list(REPO_CONFIG.keys()), value="Core (Clean)", label="Dataset") | |
| sp_sel = gr.Dropdown(["train"], value="train", label="Split") | |
| data_view = gr.Dataframe(interactive=False) | |
| with gr.Row(): | |
| c_raw = gr.Code(label="Source QASM", language="python") | |
| c_tr = gr.Code(label="Transpiled QASM", language="python") | |
| with gr.TabItem("🤖 ML Training"): | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| ml_ds_sel = gr.Dropdown(list(REPO_CONFIG.keys()), value="Core (Clean)", label="Select Dataset") | |
| # Dynamic metrics list extracted from CSV | |
| ml_feat_sel = gr.CheckboxGroup(label="Available Metrics (extracted from CSV)", choices=[]) | |
| train_btn = gr.Button("Execute Baseline", variant="primary") | |
| with gr.Column(scale=2): | |
| p_out = gr.Plot() | |
| t_out = gr.Markdown() | |
| with gr.TabItem("📖 Methodology"): | |
| meth_ds_sel = gr.Dropdown(list(REPO_CONFIG.keys()), value="Core (Clean)", label="Dataset Details") | |
| meth_md = gr.Markdown() | |
| gr.Markdown(f""" | |
| --- | |
| ### 🔗 Project Links | |
| [**🌐 Website**](https://qsbench.github.io) | [**🤗 Hugging Face**](https://huggingface.co/QSBench) | [**💻 GitHub**](https://github.com/QSBench) | |
| """) | |
| # --- EVENTS --- | |
| # Explorer | |
| ds_sel.change(update_explorer, [ds_sel, sp_sel], [sp_sel, data_view, c_raw, c_tr, meta_txt]) | |
| # ML Tab: Dynamic metrics update | |
| ml_ds_sel.change(sync_ml_metrics, [ml_ds_sel], [ml_feat_sel]) | |
| train_btn.click(train_model, [ml_ds_sel, ml_feat_sel], [p_out, t_out]) | |
| # Methodology | |
| meth_ds_sel.change(get_methodology_content, [meth_ds_sel], [meth_md]) | |
| # Initial Load | |
| demo.load(update_explorer, [ds_sel, sp_sel], [sp_sel, data_view, c_raw, c_tr, meta_txt]) | |
| demo.load(sync_ml_metrics, [ml_ds_sel], [ml_feat_sel]) | |
| demo.load(get_methodology_content, [meth_ds_sel], [meth_md]) | |
| if __name__ == "__main__": | |
| demo.launch() |