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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"]
publication_types = ["8"]
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."
selected = false
publication = "*US Patent App. US18/526120*"
tags = ["artificial neural network", "neural network model", "reinforcement learning", "quantum circuit", "transpilation"]
projects = ["qiskit-ibm-transpiler"]
url_pdf = "https://ppubs.uspto.gov/pubwebapp/authorize.html?redirect=print/pdfRedirectDownload/20250181988"
url_source = "https://patentcenter.uspto.gov/applications/18526120"
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