Training microwave pulses using quantum machine learning
Nola, Jaden Sanchez, Uriah Murthy, Anusha Krishna Steck, James E. Behrman, Elizabeth C.
Nola, Jaden
Sanchez, Uriah
Murthy, Anusha Krishna
Steck, James E.
Behrman, Elizabeth C.
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Issue Date
2024
Type
Article
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Keywords
Quantum machine learning,Quantum machine learning,Quantum computing,Quantum circuit,Error reduction
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Citation
Nola, J., Sanchez, U., Murthy, A. K., Behrman, E., & Steck, J. (2024). Training microwave pulses using quantum machine learning. arXiv preprint arXiv:2409.03861.
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.
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Publisher
Cornell Tech
Journal
arXiv