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README.md

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+---
+annotations_creators:
+- expert-generated
+language:
+- en
+language_creators:
+- machine-generated
+license:
+- mit
+multilinguality:
+- monolingual
+pretty_name: Q-Route Benchmark Dataset
+size_categories:
+- n<1K
+source_datasets:
+- original
+tags:
+- quantum-computing
+- openqasm
+- hardware-compilation
+- topology-routing
+- benchmark
+- graph-calculus
+- auto-scientist
+- adaption-labs
+task_categories:
+- text-generation
+- code-generation
+---
+
+# ⚛️ Q-Route Benchmark Dataset (`qroute-benchmark-dataset`)
+
+[![HuggingFace Dataset](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Dataset-blue)](https://huggingface.co/datasets/jay2219/Q-Route-Benchmark)
+
+The **Q-Route Benchmark Dataset** is a curated, post-training held-out evaluation suite comprising **100 non-overlapping quantum circuit routing test cases**. It is specifically designed to audit and benchmark large language models (LLMs) on **spatial hardware graph routing** for physical Quantum Processing Units (QPUs).
+
+---
+
+## 📌 Dataset Summary
+
+Physical quantum chips (such as IBM Heavy-Hex, linear chains, rings, or 2D grid lattices) restrict 2-qubit operations (`cx`, `cz`) to physically adjacent hardware qubits defined by a coupling map. 
+
+This dataset provides:
+- **Abstract Input OpenQASM 2.0 Circuits:** Uncompiled circuits containing arbitrary multi-qubit interactions across non-adjacent physical qubits.
+- **Physical Hardware Specifications:** Target QPU topology graphs, qubit counts, and explicit coupling map edge lists.
+- **Gold-Standard Ground Truth Outputs:** Provably compliant, SWAP-routed OpenQASM 2.0 circuits with minimal SWAP overhead and circuit depth.
+
+---
+
+## 📊 Dataset Statistics & Topology Breakdown
+
+The dataset contains exactly **100 benchmark test cases** balanced across 5 distinct hardware graph topologies and 3 difficulty tiers:
+
+| Hardware Topology | QPU Architecture | Physical Qubits | Coupling Edges | Test Case Count | Difficulty Tier |
+| :--- | :--- | :---: | :---: | :---: | :---: |
+| **Ring-8** | Closed 1D Ring Lattice | 8 | 16 | 20 | Easy |
+| **Linear-9** | 1D Line Chain | 9 | 16 | 20 | Medium |
+| **Grid-4x3** | 2D Rectangular Grid Lattice | 12 | 34 | 20 | Medium |
+| **Star-15** | Star / Central Hub Routing Graph | 15 | 28 | 20 | Hard |
+| **HeavyHex-16** | IBM Heavy-Hex Connectivity Lattice | 16 | 36 | 20 | Hard |
+
+**Total:** 100 benchmark circuits | 5 hardware topologies | 3 difficulty tiers (Easy: 5–8 qubits, Medium: 9–12 qubits, Hard: 13–16 qubits).
+
+---
+
+## 🗂️ Data Structure & Schema
+
+Each sample in `quantum_benchmark.jsonl` follows standard OpenAI Chat JSONL formatting with explicit metadata fields:
+
+```json
+{
+  "messages": [
+    {
+      "role": "system",
+      "content": "You are Q-Route, an enterprise-grade deterministic Quantum Hardware Compiler..."
+    },
+    {
+      "role": "user",
+      "content": "### Physical Hardware Specification:\n- Target Topology: Grid-4x3\n- Number of Qubits: 12\n- Valid Physical Edges (Coupling Map): [[0, 1], [0, 3], ...]\n\n### Abstract Input OpenQASM 2.0:\n```qasm\nOPENQASM 2.0;\ninclude \"qelib1.inc\";\nqreg q[12];\n...\n```"
+    },
+    {
+      "role": "assistant",
+      "content": "OPENQASM 2.0;\ninclude \"qelib1.inc\";\nqreg q[12];\n...\nswap q[2], q[1];\nswap q[1], q[4];\ncx q[4], q[7];\n..."
+    }
+  ],
+  "metadata": {
+    "num_qubits": 12,
+    "topology": "Grid-4x3",
+    "num_edges": 34
+  }
+}
+```
+
+### Key Fields:
+- **`messages[0]` (`system`)**: Verbatim prompt template system message enforcing strict OpenQASM compiler output.
+- **`messages[1]` (`user`)**: Complete prompt string containing target topology, qubit count, coupling map JSON, hardware constraints, and input abstract OpenQASM 2.0.
+- **`messages[2]` (`assistant`)**: Ground truth, topology-compliant OpenQASM 2.0 output with minimal SWAP gate insertions.
+- **`metadata`**: Machine-readable metadata dict containing `num_qubits`, `topology`, and `num_edges`.
+
+---
+
+## 🔒 Test Set Integrity & Audit Trail
+
+The 100 benchmark test cases in this repository were **generated AFTER training data collection was finalized**. 
+
+- **Zero Overlap:** Shared 0% overlap with the 15,000-sample training dataset ([`adaptions/qroute-training-data`](https://huggingface.co/datasets/adaptions/qroute-training-data)).
+- **Post-Training Generation:** Guaranteed post-training test set generation to prevent data contamination or benchmark memorization.
+- **Verifiable Timestamps:** Hugging Face commit history and GitHub repository creation timestamps provide an immutable audit trail.
+
+---
+
+## 🚀 How to Load and Use
+
+### Using `datasets` in Python:
+
+```python
+from datasets import load_dataset
+
+# Load the benchmark dataset from Hugging Face Hub
+dataset = load_dataset("jay2219/Q-Route-Benchmark")
+
+# Inspect a sample test case
+sample = dataset["train"][0]
+print("Topology:", sample["metadata"]["topology"])
+print("Qubits:", sample["metadata"]["num_qubits"])
+print("User Prompt:\n", sample["messages"][1]["content"])
+```
+
+
+## 📜 Citation & License
+
+This dataset is released under the **MIT License**.
+
+```bibtex
+@dataset{qroute_benchmark_dataset_2026,
+  author       = {Jay Prajapati},
+  title        = {Q-Route Benchmark Dataset: 100 Held-Out Quantum Circuit Routing Evaluation Cases},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/datasets/jay2219/Q-Route-Benchmark}}
+}
+```