---
aliases:
  - '/publication/2025-patent_rl_transpilation/'
title: "Reinforcement learning based transpilation of quantum circuits"
date: 2025-06-05
authors:
  - "David Kremer"
  - "Juan Cruz-Benito"
  - "Victor Villar"
  - "Hanhee Paik"
  - "Ismael Faro Sertage"
  - "Ivan Duran Martinez"
  - "Francisco Jose Martin Fernandez"
  - "Alexandra Rivero Garcia"
abstract: "Systems and techniques that facilitate quantum circuit transpiling are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory that can execute the computer executable components stored in memory. The computer executable components can comprise a receiver component that receives an input quantum circuit representation and one or more quantum circuit constraints, a machine learning component that generates a transpiled quantum circuit representation based on the one or more quantum circuit constraints and the input quantum circuit representation."
publication_types:
  - "patent"
selected: false
publication: "*US Patent App. US18/526120*"
tags:
  - "artificial neural network"
  - "neural network model"
  - "reinforcement learning"
  - "quantum circuit"
  - "transpilation"
projects:
  - "qiskit-ibm-transpiler"
links:
- type: pdf
  url: "https://ppubs.uspto.gov/pubwebapp/authorize.html?redirect=print/pdfRedirectDownload/20250181988"
- type: source
  url: "https://patentcenter.uspto.gov/applications/18526120"
---