quantumopt 0.1.0

AI-driven quantum circuit compiler using GNN + LLM

quantumopt

AI-driven quantum circuit compiler using Graph Neural Networks + Claude LLM — tested on real research circuits

What It Does

quantumopt takes your quantum circuit and returns an optimized version that runs more accurately on IBM quantum hardware — with a plain English explanation you can cite in your paper.

Installation

pip install quantumopt

Usage

from qiskit import QuantumCircuit
from quantumopt import compile

# Your research circuit
qc = QuantumCircuit(5)
qc.h(0)
qc.cx(0, 1)
qc.cx(1, 2)
qc.ry(0.5, 0)
qc.rz(0.3, 1)

# Compile and optimize
result = compile(qc, hardware="ibm_brisbane")

# Results
print(result.depth_reduction)    # "31%"
print(result.gate_reduction)     # "32%"
print(result.explanation)        # Claude-generated report
print(result.optimized_circuit)  # Ready to run on IBM

Benchmark Results

Tested on 41 real circuits from QASMbench (published research circuits):

Metric	quantumopt	Baseline
Avg depth reduction	13.2%	0%
Avg gate reduction	15.2%	0%
Circuits improved	34/41	N/A
Circuits made worse	0/41	N/A
Best result	89%	N/A

Tested on 10,240 synthetic circuits:

Metric	Result
GNN prediction accuracy (±10%)	82%
GNN prediction accuracy (±20%)	100%
Avg predicted improvement	64.5%

Example Explanation Output

When ANTHROPIC_API_KEY is set, quantumopt generates research-quality explanations:

"Transpilation of the target circuit for IBM Brisbane hardware yielded a 31.6% reduction in circuit depth (128 → 88 layers) and a 31.6% reduction in total gate count (326 → 223 gates). Among the applied optimization passes, merge_rotations contributed most substantially, as consecutive single-qubit rotation gates collapse into single parametrized operations. The resulting reduction in two-qubit gate exposure is particularly consequential for hardware execution, as each eliminated layer directly reduces coherence-time consumption against Brisbane's median T₂ timescales (~100–200 µs)."

How It Works

1. Your circuit is encoded as a Directed Acyclic Graph
2. A trained GNN (82% accuracy) predicts optimization potential and recommends optimization actions
3. Qiskit transpiler optimizes for target hardware
4. Claude generates a hardware-specific explanation you can cite in your paper

Supported Algorithms

VQE, QAOA, QFT, Grover, GHZ, Bernstein-Vazirani, Deutsch-Jozsa, Amplitude Estimation, Phase Estimation

Supported Hardware

• IBM Brisbane (default)
• More backends coming

Configuration

# Required for AI explanations
export ANTHROPIC_API_KEY=your-key-here

# Optional — for real IBM hardware execution
export IBM_QUANTUM_TOKEN=your-token-here

Requirements

• Python 3.10+
• Qiskit >= 1.0.0
• PyTorch >= 2.0.0
• torch-geometric >= 2.4.0
• anthropic >= 0.20.0 (optional, for explanations)

Citation

If you use quantumopt in your research please cite:

Syamala, N. (2025). quantumopt: An AI-driven quantum 
circuit compiler using Graph Neural Networks and Large 
Language Models. GitHub.
https://github.com/nsyamala1/quantumopt

License

MIT License — free for research and commercial use.

Contact

Built by Naveen Syamala GitHub: github.com/nsyamala1 Issues: github.com/nsyamala1/quantumopt/issues