Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving and often expediting classical machine learning techniques. Such …

Main QML method for noisy intermediate-scale quantum (NISQ) devices • Structure similar to other modern quantum algorithms: e.g. variational quantum eigensolver (VQE), quantum …

Classical algorithms for ML problems typically take polynomial time in the size of the training set and number of features (the dimension of the space). exponential speed-up with quantum …

Feb 3, 2025 · For self-consistency, this tutorial covers foundational principles, representative QML algorithms, their potential applications, and critical aspects such as trainability, generalization, …

Quantum Machine Learning • AI/ML already uses special-purpose processors: GPUs, TPUs, ASICs • Quantum computers (QPUs) could be used as special-purpose AI accelerators • May …

Oct 11, 2024 · Quantum Machine Learning (QML) is an interdisciplinary field that combines quantum computing with machine learning. It aims to harness the power of quantum …

Figure 1 demonstrates the flow chart of the hybrid model: (1) The quantum data to be classified which are single qubits are generated; (2) Quantum operation (Parameterized rotation gate) is...

Nov 15, 2021 · Quantum machine learning (QML) investigates approaches and techniques that use synergies between these two fields. This work aims at outlining the possibilities given by …

Jan 22, 2022 · This tutorial provides an overview of Quantum Machine Learning (QML), a relatively novel discipline that brings together concepts from Machine Learning (ML), Quantum …

This paper presents the literature describing implementations of quantum machine learning algorithms in the various quantum machine learning frameworks. In addition, for each of the ob …