---
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
---

# ⚛️ 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}}
}
```