---
title: "Routing quantum circuits using machine learning"
date: 2026-08-13
authors:
  - "Daniel Josef Egger"
  - "Elena Peña Tapia"
  - "Stefan Woerner"
  - "David Kremer Garcia"
  - "Juan Cruz-Benito"
abstract: "A method, computer program product, and system for routing quantum circuits. A structure of a quantum circuit to route is encoded in an environment using a reinforcement learning agent. A noise of a built routed quantum circuit is encoded in a cost function, such as by measuring the noise in the layer(s) of gates of the built routed quantum circuit. The structure of a quantum circuit refers to the sequence of quantum gates applied to qubits. A cost function refers to a mathematical function that measures how well the predictions of the machine learning model align with the actual target values. Quantum circuit routing is then performed using a trained machine learning model based on the structure of the quantum circuit and minimizing the cost function, which reduces the gate count and noise since the structure facilitates the routing task and noise minimization as encoded in the cost function."
publication_types:
  - "patent"
selected: false
publication: "*US Patent App. US20260236819A1*"
tags:
  - "quantum computing"
  - "quantum circuit routing"
  - "reinforcement learning"
  - "machine learning"
  - "transpilation"
projects:
  - "qiskit-ibm-transpiler"
links:
- type: pdf
  url: "https://ppubs.uspto.gov/pubwebapp/authorize.html?redirect=print/pdfRedirectDownload/20260236819"
- type: source
  url: "https://patentcenter.uspto.gov/applications/19068882"
---