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.…