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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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On the Applicability of Quantum Machine Learning.

Sebastian Raubitzek1,2, Kevin Mallinger1,2

  • 1Data Science Research Unit, TU Wien, Favoritenstrasse 9-11/194, 1040 Vienna, Austria.

Entropy (Basel, Switzerland)
|July 29, 2023
PubMed
Summary
This summary is machine-generated.

Quantum machine learning classifiers like variational quantum circuits and quantum kernel estimators show promise but currently underperform advanced classical methods. Further research is needed to optimize quantum approaches for broader applicability and quantum advantage.

Keywords:
CatBoostLassoLightGBMQiskitRidgeXGBoostboost classifiersclassificationneural networksquantum computingquantum kernel estimatorquantum machine learningvariational quantum circuit

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Data Science

Background:

  • Quantum machine learning (QML) offers potential for enhanced computational tasks.
  • Classical machine learning algorithms are highly optimized and widely used.

Purpose of the Study:

  • To evaluate the performance of quantum classifiers (variational quantum circuit and quantum kernel estimator) for classification tasks.
  • To compare QML performance against classical algorithms on benchmark and novel datasets.
  • To explore data structures suitable for quantum advantage.

Main Methods:

  • Implemented and evaluated variational quantum circuit (VQC) and quantum kernel estimator (QKE) using Qiskit.
  • Conducted hyperparameter searches on six benchmark datasets and analyzed performance with varying sample sizes on artificial datasets.
  • Introduced a novel dataset based on quantum mechanics concepts.

Main Results:

  • VQC and QKE outperformed basic linear models but lagged behind advanced classical classifiers (XGBoost, LightGBM, CatBoost) in accuracy and runtime.
  • Classical methods showed superior performance on datasets with group structures compared to quantum approaches utilizing unitary processes.
  • Quantum simulator, feature map, and circuit choices significantly impact QML estimator performance.

Conclusions:

  • QML algorithms show future potential but currently do not match state-of-the-art classical methods.
  • Classical machine learning excels with group-structured data over current quantum methods.
  • Transparency in QML hyperparameter selection is crucial for reproducibility and advancement.