---
aliases:
  - '/publication/2025-patent_rl_cliffords/'
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"
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."
publication_types:
  - "patent"
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"
links:
- type: pdf
  url: "https://ppubs.uspto.gov/pubwebapp/authorize.html?redirect=print/pdfRedirectDownload/20250021853"
- type: source
  url: "https://patentcenter.uspto.gov/applications/18466323"
---