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jay2219/quantum-circuit-routing

This dataset is a remastered version prepared using Adaption's Adaptive Data platform. quantum_circuit_routing This dataset contains pairs of abstract OpenQASM 2.0 quantum circuits and their hardware-compliant compiled versions. Each sample specifies a target qubit topology (e.g., Star, Ring, Linear, Grid, HeavyHex) and coupling map constraints. The compiled outputs include necessary SWAP gates inserted to satisfy physical connectivity requirements while minimizing circuit… See the full description on the dataset page: https://huggingface.co/datasets/jay2219/quantum-circuit-routing.

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This dataset is a remastered version prepared using Adaption's Adaptive Data platform.

quantumcircuitrouting

This dataset contains pairs of abstract OpenQASM 2.0 quantum circuits and their hardware-compliant compiled versions. Each sample specifies a target qubit topology (e.g., Star, Ring, Linear, Grid, HeavyHex) and coupling map constraints. The compiled outputs include necessary SWAP gates inserted to satisfy physical connectivity requirements while minimizing circuit depth.

Dataset Motto: "Embedding physical spatial graph calculus into LLM weight space to eliminate graph hallucinations and enable deterministic quantum hardware circuit routing."

Dataset size

There are 15,000 data points in this dataset. This is an instruction tuning dataset.

Quality of Remastered Dataset

The final quality is B, with a relative quality improvement of -26.0%.

Domain

  • —Code (80%)
  • —Science (20%)

Language

  • —English (100%)

Tone

  • —Technical (100%)

Evaluation Results

  • —Quality Gains: <img src="https://proteus-prod-public.s3.us-east-1.amazonaws.com/temp/3e335eb7-a195-4de4-94f9-4b3f0a221690.png" alt="QualityGains" style="max-width: 50%; display: block; margin-left: auto; margin-right: auto;" />
  • —Grade Improvement: <img src="https://proteus-prod-public.s3.us-east-1.amazonaws.com/temp/b9a712cd-8a96-47e0-b5a7-5b1d683479e9.png" alt="Grade" style="max-width: 50%; display: block; margin-left: auto; margin-right: auto;" />
  • —Percentile Chart: <img src="https://proteus-prod-public.s3.us-east-1.amazonaws.com/temp/52761e13-0dea-4c29-a368-9cbac7fec0fe.png" alt="Percentile Chart" style="max-width: 50%; display: block; margin-left: auto; margin-right: auto;" />

Usage & Quickstart

Load the dataset directly using the Hugging Face datasets library:

python
from datasets import load_dataset

# Load the instruction-tuning dataset
dataset = load_dataset("jay2219/quantum-circuit-routing")

# Inspect a sample training pair
sample = dataset["train"][0]
print("System Role:\n", sample["messages"][0]["content"])
print("User Prompt (Abstract QASM & Topology):\n", sample["messages"][1]["content"])
print("Assistant Response (Routed Compliant QASM):\n", sample["messages"][2]["content"])

Data Schema & Fields

Each record follows the standard OpenAI Chat format:

  • —`messages[0]` (`system`): Enforces deterministic quantum hardware compilation role and strict OpenQASM output requirements.
  • —`messages[1]` (`user`): Specifies the physical hardware topology, qubit count, explicit coupling map (valid edge list), hardware constraints, and input abstract OpenQASM 2.0.
  • —`messages[2]` (`assistant`): Compiled OpenQASM 2.0 code containing minimal SWAP gate insertions to satisfy physical coupling map adjacency constraints.

Hardware Topologies Covered

The 15,000 instruction-tuning samples span 5 distinct physical quantum hardware architectures:

  1. 1.Linear: 1D line chain topology (e.g. 5–15 qubits).
  2. 2.Ring: 1D closed-loop ring topology.
  3. 3.Star: Central hub routing graph (all peripheral qubits connect through hub node 0).
  4. 4.Grid: 2D rectangular lattice QPU connectivity graphs (e.g. 2x2 up to 5x3).
  5. 5.HeavyHex: IBM Heavy-Hex lattice architecture.

Associated Models & Evaluation Benchmarks