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Data & Code: Quantum receiver enhanced by adaptive learning

Chaohan Cui, Zheshen Zhang

Published Nov 30, 2022• 0 citations
Publisher

Abstract

This folder contains original data, data processing code, and demo code for the paper entitled "Quantum receiver enhanced by adaptive learning" published in Light: Science & Applications, DOI: 10.1038/s41377-022-01039-5. Please contact chaohancui@arizona.edu if you have questions or other concerns.

DOI
10.5281/zenodo.7382846

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This folder contains original data, data processing code, and demo code for the paper entitled "Quantum receiver enhanced by adaptive learning" published in Light: Science & Applications, DOI: 10.1038/s41377-022-01039-5. Please contact chaohancui@arizona.edu if you have questions or other concerns.
This data repository contains the information necessary to reproduce the main results of the paper “Exponential quantum advantage for learning signals with a single qubit”. The repository contains both the data required to generate the figures of the main manuscript and the supplemental material, as well as source code for running the experiments and simulations presented in the paper. There are two main folders: experiment_code (which contains all experimental scripts to calibrate device and run the protocols presented in the paper) and simulation_code (which contains simulation details of the dark matter and communication detection tasks) The data folder contains experimental raw and processed data generated. The figures folder contains all the plots presented in the manuscript.
This data repository contains the information necessary to reproduce the main results of the paper “Quantum computational displacement sensing”. The repository contains both the data required to generate the figures of the main manuscript and the supplemental material, as well as source code for running the experiments and simulations presented in the paper. There are two main folders: code_simulation (which consists of the training code and the baseline simulations) and code_experiment (which covers the experimental code). The corresponding requirements for running them are in the zipped folder. The data folder contains all simulation and experimental raw and processed data generated. The figures folder contains all the plots presented in the manuscript.
This paper introduces a novel data augmentation technique based on quantum machine learning, termed Adaptive Data Enhancement (ADE). The core principle of ADE lies in its ability to dynamically adjust data augmentation strategies based on the inherent changes in the input data distribution. Traditional data augmentation methods rely on predefined parameters, which often struggle to effectively capture the complexities of real-world data. ADE leverages quantum machine learning algorithms to perform this adaptation in a computationally efficient manner, resulting in improved model generalization and enhanced machine learning performance. The proposed method offers a significant advancement over existing techniques by providing a mechanism for automated and responsive data enhancement, addressing a critical challenge in the field of machine learning.