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title = "Reinforcement Learning based Clifford Circuit Synthesis"
date = 2025-01-16
authors = ["Juan Cruz-Benito",  "David Kremer", "Hanhee Paik", "Ismael Faro Sertage",  "Francisco Jose Martin Fernandez", "Ivan Duran Martinez", "Sanjay Vishwakarma", "Vipul Sharma"]
publication_types = ["8"]
abstract = "Systems and techniques that facilitate Clifford circuit synthesis 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 a quantum circuit design comprising a Clifford circuit representation and one or more circuit restrictions, and a machine learning component that generates, using a machine learning model, a replacement circuit based on the one or more circuit restrictions and the Clifford circuit representation, and generates a modified quantum circuit design by replacing the Clifford circuit representation with the replacement circuit."
selected = false
publication = "*US Patent App. US18/466323*"
tags = ["artificial neural network", "neural network model", "reinforcement learning", "quantum circuit", "synthesis", "clifford circuit"]
projects = ["qiskit-ibm-transpiler"]
url_pdf = "https://ppubs.uspto.gov/pubwebapp/authorize.html?redirect=print/pdfRedirectDownload/20250021853"
url_source = "https://patentcenter.uspto.gov/applications/18466323"
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