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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
---
# ⚛️ Q-Route Benchmark Dataset (`qroute-benchmark-dataset`)
[](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}}
}
```
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