Reducing single-qubit gate complexity using machine-learned microwave pulses

2025/05/09 by Jaden Nola, Uriah Sanchez, A. K. MURTHY +2 · 1 voice
Computer Science · #Quantum Computing Algorithms and Architecture #Quantum-Dot Cellular Automata #Quantum Information and Cryptography

paper · doi:10.20935/acadquant7692

openalex publication_date 2025/05/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Abstract

A gate sequence of single-qubit transformations may be condensed into a single microwave pulse that maps a qubit from an initialized state directly into the desired state of the composite transformation. Here, machine learning is used to learn the parameterized values for a single driving pulse associated with a transformation of three sequential gate operations on a qubit. This implies that future quantum circuits may contain roughly a third of the number of single-qubit operations performed, greatly reducing the problems of noise and decoherence. There is a potential for even greater condensation and efficiency using the methods of quantum machine learning.

Citations

• Quantum machine learning
• Supervised quantum machine learning models are kernel methods
• Hybrid Gate-Pulse Model for Variational Quantum Algorithms
• Quantum computing with Qiskit

Discussions

• This paper was just published in Academia Quantum! doi.org/10.20935/Aca... with Jaden Nola, Uriah Sanchez, Anusha Krishna Murthy, Elizabeth Behrman, James Steck #AcademicBluesky [bsky, 0 points, 0 comments]