TL;DR: I built quantumopt , an open-source AI-powered quantum circuit compiler using a Graph Attention Network. It achieves 30–48% gate reduction on real VQE and IQP circuits. Here’s how it works, what I got wrong, and what I learned. Why I Built This I’m a CS student who got obsessed with quantum computing. The problem I kept running into: quantum circuits need to be compiled down to hardware-specific gate sets before running on real IBM machines — and this compilation step massively affects circuit quality. Qiskit’s built-in transpiler is good. But it doesn’t predict whether a circuit is worth optimizing before trying. I wondered: what if a GNN could look at a circuit’s structure and predict optimization potential before the transpiler runs? That became quantumopt . How It Works The pipeline has three stages: 1. Circuit → Graph Every quantum circuit is a directed acyclic graph. Gates become nodes, dependencies become edges.…