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85ae166 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 | # 🌌 QSBench: Entanglement Score Regression Guide
Welcome to the **QSBench Regression Hub**.
This tool demonstrates how Machine Learning can predict the **degree of quantum entanglement** — measured by the **Meyer–Wallach score** — using only circuit structure and topology.
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## ⚠️ Important: Demo Dataset Notice
This Space uses **demo shards** of the QSBench datasets.
- **Limited size:** The dataset is intentionally reduced.
- **Impact:** Model performance may be unstable or noisy.
- **Goal:** Showcase how structural features correlate with entanglement — not achieve production-level accuracy.
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## 🧠 1. What is Being Predicted?
The model predicts:
### `meyer_wallach`
A continuous entanglement measure:
- **0.0 → No entanglement**
- **1.0 → Maximum entanglement**
This metric captures how strongly qubits are globally correlated in a circuit.
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## 🧩 2. How the Model “Sees” a Circuit
The model does **not simulate quantum states**.
Instead, it uses **structural proxies**:
### 🔹 Topology Features
- `adj_density` — how densely qubits interact
- `adj_degree_mean` — average connectivity
- `adj_degree_std` — variability in connectivity
→ These reflect **entanglement potential** in the circuit graph.
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### 🔹 Gate Structure
- `total_gates`
- `single_qubit_gates`
- `two_qubit_gates`
- `cx_count`
→ Two-qubit gates are the **primary drivers of entanglement**.
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### 🔹 Complexity Metrics
- `depth`
- `gate_entropy`
→ Capture how “deep” and “structured” the circuit is.
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### 🔹 QASM-derived Signals
- `qasm_length`
- `qasm_line_count`
- `qasm_gate_keyword_count`
→ Lightweight proxies for circuit complexity without parsing semantics.
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## 🤖 3. Model Overview
The system uses:
### Random Forest Regressor
- Works well on tabular data
- Handles non-linear relationships
- Provides feature importance
Pipeline includes:
- Missing value imputation
- Feature scaling
- Ensemble regression
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## 📊 4. Understanding the Results
After clicking **"Train & Evaluate"**, you get:
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### A. Actual vs Predicted
- Each point = one circuit
- Diagonal line = perfect prediction
→ The closer to the line → the better
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### B. Residual Distribution
- Shows prediction errors
- Centered around 0 → good model
→ Wide spread = uncertainty or weak features
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### C. Feature Importance
Top contributing features to prediction.
Typical patterns:
- `cx_count` → strong signal
- `adj_density` → topology influence
- `depth` → complexity contribution
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## 📉 5. Metrics Explained
- **RMSE** — penalizes large errors
- **MAE** — average absolute error
- **R²** — goodness of fit (1 = perfect)
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## 🧪 6. Experimentation Tips
Try:
- Removing `cx_count` → see how performance drops
- Using only topology → isolate structural effect
- Increasing trees → more stable predictions
- Changing test split → robustness check
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## 🔬 7. Key Insight
> Entanglement is not random — it is encoded in circuit structure.
Even without simulation:
- Gate distribution
- Connectivity
- Depth
…already contain enough signal to estimate entanglement.
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## 🔗 8. Project Resources
- 🤗 Hugging Face: https://huggingface.co/QSBench
- 💻 GitHub: https://github.com/QSBench
- 🌐 Website: https://qsbench.github.io |