Quantum Machine Learning: A Review and Case Studies.

Area of Science:

• Computer Science
• Quantum Computing
• Machine Learning

Background:

• Classical machine learning models require significant computational resources, necessitating high-speed hardware.
• The growing trend in computational demands drives research into quantum computing for machine learning applications.
• A comprehensive review of quantum machine learning (QML) accessible to those without a physics background is needed.

Purpose of the Study:

• To provide a review of quantum machine learning from the perspective of conventional machine learning techniques.
• To outline a research path from quantum theory to QML algorithms for computer scientists.
• To discuss fundamental QML algorithms and their implementation.

Main Methods:

• Implemented Quanvolutional Neural Networks (QNNs) on a quantum computer for handwritten digit recognition.
• Compared QNN performance against classical Convolutional Neural Networks (CNNs).
• Implemented Quantum Support Vector Machines (QSVM) on a breast cancer dataset and compared against classical Support Vector Machines (SVM).
• Implemented Variational Quantum Classifiers (VQC) and various classical classifiers on the Iris dataset for accuracy comparison.

Main Results:

• Performance comparison of QNNs vs. CNNs for digit recognition.
• Performance comparison of QSVMs vs. classical SVMs on breast cancer data.
• Accuracy comparison of VQC and classical classifiers on the Iris dataset.

Conclusions:

• Quantum machine learning algorithms show promise as alternatives to classical methods.
• Comparative studies are essential for understanding the practical advantages of QML algorithms.
• Further research is needed to fully explore the potential of QML in various applications.