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Trapped Ion Quantum Computing Quantum Machine Learning

On nonlinear transformations in quantum computation

arXiv

Authors: Zoë Holmes, Nolan Coble, Andrew T. Sornborger, Yiğit Subaşı

Year

2021

Paper ID

40322

Status

Preprint

Abstract Read

~2 min

Abstract Words

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Abstract

While quantum computers are naturally well-suited to implementing linear operations, it is less clear how to implement nonlinear operations on quantum computers. However, nonlinear subroutines may prove key to a range of applications of quantum computing from solving nonlinear equations to data processing and quantum machine learning. Here we develop a series of basic subroutines for implementing nonlinear transformations of input quantum states. Our algorithms are framed around the concept of a weighted state, a mathematical entity describing the output of an operational procedure involving both quantum circuits and classical post-processing.

Why This Paper Matters

This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2021 reference point for readers tracking recent quantum research.
While quantum computers are naturally well-suited to implementing linear operations, it is less clear how to implement nonlinear operations on quantum computers.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2112.12307 [2] arXiv https://arxiv.org/abs/2112.12307 [3] Publisher https://arxiv.org/abs/2112.12307

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