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The lecture aims to provide an overview of quantum machine learning techniques, specifically QNNs and QSVMs.

April 18, 2026Open Access

Quantum Machine Learning

Key Points

The lecture aims to provide an overview of quantum machine learning techniques, specifically QNNs and QSVMs.
Introduced quantum neural networks as variational quantum circuits.
Discussed challenges in training QNNs, particularly the barren plateau phenomenon.
Explored Quantum Support Vector Machines and their use of quantum feature maps.
Compared the strengths and weaknesses of QNNs and QSVMs in the current landscape.
Identified practical issues that hinder effective optimization in QNNs.
Outlined how QSVMs encode classical data into quantum spaces.
Presented insights into the potential and required research for better QML applications.

Abstract

This lecture provides an overview of quantum machine learning (QML), focusing on two prominent paradigms: Quantum Neural Networks (QNNs) and Quantum Support Vector Machines (QSVMs). We begin by introducing QNNs, exploring their structure as variational quantum circuits and the challenges associated with their training. We will cover key practical issues such as the barren plateau phenomenon, which can hinder effective gradient-based optimization. Next, the lecture will turn to Quantum Support Vector Machines, an algorithm that leverages quantum feature maps to encode classical data into a high-dimensional quantum Hilbert space. We will conclude by comparing the strengths and weaknesses of both QNNs and QSVMs in the current QML landscape, offering a realistic perspective on their potential and the research needed to realize their full capabilities.

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