AI Methods for Approximate Compiling of Unitaries Paper Published

Excited to share our latest research: “AI methods for approximate compiling of unitaries”! 🚀

This work explores how artificial intelligence can make quantum circuit compilation more efficient. We focus on superconducting quantum hardware using fixed two-qubit gates and single-qubit rotations.

🔍 Our approach:

1. Three-stage process: identifying templates, predicting parameters, and refining through gradient descent
2. Uses deep learning and autoencoder-like models to suggest initial templates and parameter values
3. Demonstrates improvements over exhaustive search and random initialization on 2 and 3-qubit unitaries

This research highlights AI’s potential to enhance quantum circuit transpiling, supporting more efficient quantum computations on current and future hardware.

The paper has been accepted at QCE24 (Fifth IEEE International Conference on Quantum Computing and Engineering)!

Read the paper: https://arxiv.org/abs/2407.21225v1

Originally shared on LinkedIn on August 7, 2024 - 58 reactions, 0 comments as of 11/12/2025