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Trapped Ion Quantum Computing Quantum Machine Learning Quantum Simulation

Supervised Quantum Learning without Measurements

arXiv

Authors: Unai Alvarez-Rodriguez, Lucas Lamata, Pablo Escandell-Montero, José D. Martín-Guerrero, Enrique Solano

Year

2016

Paper ID

41786

Status

Preprint

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~2 min

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Abstract

We propose a quantum machine learning algorithm for efficiently solving a class of problems encoded in quantum controlled unitary operations. The central physical mechanism of the protocol is the iteration of a quantum time-delayed equation that introduces feedback in the dynamics and eliminates the necessity of intermediate measurements. The performance of the quantum algorithm is analyzed by comparing the results obtained in numerical simulations with the outcome of classical machine learning methods for the same problem. The use of time-delayed equations enhances the toolbox of the field of quantum machine learning, which may enable unprecedented applications in quantum technologies.

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This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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We propose a quantum machine learning algorithm for efficiently solving a class of problems encoded in quantum controlled unitary operations.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:1612.05535 [2] arXiv https://arxiv.org/abs/1612.05535 [3] Publisher https://arxiv.org/abs/1612.05535

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