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| # 🌌 CNOT Count Regression Guide | |
| Welcome to the **CNOT Count Regression Hub**. | |
| This tool demonstrates how Machine Learning can predict the number of **CNOT (CX) gates** — the most noise-prone two-qubit operations — using only structural features of quantum circuits. | |
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| ## ⚠️ Important: Demo Dataset Notice | |
| The datasets used here are **v1.0.0-demo** shards. | |
| * **Constraint:** Reduced dataset size. | |
| * **Impact:** Model accuracy may fluctuate depending on split and features. | |
| * **Goal:** Demonstrate how circuit topology correlates with entangling gate usage. | |
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| ## 🎯 1. What is Being Predicted? | |
| The model predicts: | |
| ### `cx_count` | |
| The total number of **CNOT gates** in a circuit. | |
| Why this matters: | |
| * CNOT gates are the **main source of noise** in NISQ devices | |
| * They dominate **error rates and decoherence** | |
| * Reducing them is key to **hardware-efficient quantum algorithms** | |
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| ## 🧠 2. How the Model Works | |
| We train a **Random Forest Regressor** to map circuit features → `cx_count`. | |
| ### Input Features (examples): | |
| * **Topology:** | |
| * `adj_density` — connectivity density | |
| * `adj_degree_mean` — average qubit interaction | |
| * **Complexity:** | |
| * `depth` — circuit depth | |
| * `total_gates` — total number of operations | |
| * **Structure:** | |
| * `gate_entropy` — randomness vs regularity | |
| * **QASM-derived:** | |
| * `qasm_length`, `qasm_line_count` | |
| * `qasm_gate_keyword_count` | |
| The model learns how **structural patterns imply entangling cost**. | |
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| ## 📊 3. Understanding the Output | |
| After training, you’ll see: | |
| ### A. Actual vs Predicted Plot | |
| * Each point = one circuit | |
| * Diagonal line = perfect prediction | |
| * Spread = prediction error | |
| 👉 Tight clustering = good model | |
| --- | |
| ### B. Residual Distribution | |
| * Shows prediction errors (`actual - predicted`) | |
| * Centered around 0 = unbiased model | |
| * Wide spread = instability | |
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| ### C. Feature Importance | |
| Top features driving predictions: | |
| * High importance = strong influence on `cx_count` | |
| * Helps identify: | |
| * what increases entanglement cost | |
| * which metrics matter most | |
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| ## 🔍 4. Explorer Tab | |
| Inspect real circuits: | |
| * View dataset slices (`train`, etc.) | |
| * See raw and transpiled QASM | |
| * Understand how circuits differ structurally | |
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| ## ⚙️ 5. Tips for Better Results | |
| * Use **diverse features** (topology + QASM) | |
| * Avoid too small datasets after filtering | |
| * Tune: | |
| * `max_depth` | |
| * `n_estimators` | |
| * Try different datasets: | |
| * Noise changes structure → changes predictions | |
| --- | |
| ## 🚀 6. Why This Matters | |
| This tool helps answer: | |
| * How expensive is a circuit in terms of **entangling operations**? | |
| * Can we estimate noise **before execution**? | |
| * Which designs are more **hardware-friendly**? | |
| --- | |
| ## 🔗 7. Project Resources | |
| * 🤗 [Hugging Face](https://huggingface.co/QSBench) | |
| * 💻 [GitHub](https://github.com/QSBench) | |
| * 🌐 [Website](https://qsbench.github.io) |