Quantumopt – GNN-based quantum circuit compiler (34% gate reduction)

Name: Quantumopt – GNN-based quantum circuit compiler (34% gate reduction)
Availability: InStock
Author: Naveen_S1

by Naveen_S1·Feb 26, 2026·1 point·0 comments

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AI Analysis

●●●BangerWizardryBig BrainShip It

GNN predicts circuit optimization then explains results in research-grade prose.

Strengths

•Trained GNN (82% accuracy) + Qiskit transpiler combo is genuinely clever—predicts then applies, not guesses
•Measurable results on real QASMbench circuits (34% gate reduction, 0 made worse) vs. baselines
•Claude-generated hardware-specific explanations—citeable output, not just metrics

Weaknesses

•Niche audience: only matters if you're running circuits on IBM Brisbane hardware
•Single-week build suggests incomplete testing; no discussion of GNN generalization to other hardware

Post Description

I'm a CS student with no prior quantum computing background who built this in about a week.

quantumopt uses a Graph Attention Network trained on 10,240 quantum circuits to predict optimization potential, then passes to Qiskit's transpiler for hardware-specific compilation targeting IBM Brisbane.

Results on 41 real QASMbench circuits: - 34% average gate reduction - 28% average depth reduction - 0 circuits made worse - 82% GNN prediction accuracy

Happy to answer questions about the GNN architecture, training pipeline, or quantum compilation approach.