AI Methods for Approximate Compiling of Unitaries Paper Published
AI Methods for Approximate Compiling of UnitariesExcited 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:
- Three-stage process: identifying templates, predicting parameters, and refining through gradient descent
- Uses deep learning and autoencoder-like models to suggest initial templates and parameter values
- 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
I research and develop AI-based systems to tackle complex problems in quantum computing and other relevant areas.
My work sits at the intersection of quantum computing, machine learning, and open source. I hold IBM Master Inventor status and have contributed to tools used by millions of developers worldwide.
I have a PhD in Computer Engineering from the University of Salamanca (2018), have authored 80+ publications, and received the 2019 SCIE-BBVA Award for best young researcher in Computer Science in Spain.