---
title: 'Reinforcement Learning for Quantum Transpiling: Research Paper Published'
subtitle: 'Achieving near-optimal circuit synthesis and routing with RL'
summary: 'Published research demonstrating integration of Reinforcement Learning into quantum transpiling workflows for Qiskit transpiler service. Achieves near-optimal circuit synthesis and routing with significant performance improvements over traditional optimization methods, handling Linear Function, Clifford, and Permutation circuits up to 65 qubits.'
authors:
- juancb
tags:
- Qiskit
- Quantum Computing
- IBM Quantum
- Reinforcement Learning
- Transpilation
- AI for Quantum
- Research
categories:
- Quantum Computing
- Artificial Intelligence
- Research
date: "2024-06-17T10:00:00Z"
lastmod: "2024-06-17T10:00:00Z"
featured: true
draft: false

# Featured image
# To use, add an image named `featured.jpg/png` to your page's folder.
# Placement options: 1 = Full column width, 2 = Out-set, 3 = Screen-width
# Focal point options: Smart, Center, TopLeft, Top, TopRight, Left, Right, BottomLeft, Bottom, BottomRight
image:
  placement: 2
  caption: 'Reinforcement Learning for Quantum Transpiling'
  focal_point: "Smart"
  preview_only: false

# Projects (optional).
#   Associate this post with one or more of your projects.
#   Simply enter your project's folder or file name without extension.
#   E.g. `projects = ["internal-project"]` references `content/project/deep-learning/index.md`.
#   Otherwise, set `projects = []`.
projects: []
---

Excited to share research demonstrating the integration of Reinforcement Learning (RL) into quantum transpiling workflows for the Qiskit transpiler service! 🚀

This work achieves near-optimal circuit synthesis and routing with significant performance improvements over traditional optimization methods like SAT solvers.

Key achievements:
✅ Linear Function, Clifford, and Permutation circuit synthesis up to 65 qubits
✅ Substantial reductions in two-qubit gate depth for routing up to 133 qubits
✅ Performance advantages over SABRE routing heuristics
✅ Practical efficiency for quantum transpiling pipelines

This research represents a major step forward in making quantum computing more efficient and accessible through AI-powered optimization.

Big thanks to the amazing team: David Kremer, Víctor Villar Pascual, Hanhee Paik, Ivan Duran Martinez, and Ismael Faro!

Read the paper: https://lnkd.in/dcnw4Zav

#qiskit #quantumcomputing #IBMQuantum

---

*Originally shared on [LinkedIn](https://www.linkedin.com/posts/juancb_qiskit-quantumcomputing-ibmquantum-activity-7208407307783663616-3bfR) on June 17, 2024 - 48 reactions, 0 comments as of 11/12/2025*