Abstract Kernel methods in Quantum Machine Learning (QML) have recently gained
significant attention as a potential candidate for achieving a quantum advantage in data …

Quantum computers are known to provide speedups over classical state-of-the-art machine
learning methods in some specialized settings. For example, quantum kernel methods have …

Research on near-term quantum machine learning has explored how classical machine
learning algorithms endowed with access to quantum kernels (similarity measures) can …

Quantum support vector machines employ quantum circuits to define the kernel function. It
has been shown that this approach offers a provable exponential speedup compared to any …

Despite the mounting anticipation for the quantum revolution, the success of quantum
machine learning (QML) in the noisy intermediate-scale quantum (NISQ) era hinges on a …

Parameterized quantum circuits used as variational ansatzes are emerging as promising
tools to tackle complex problems ranging from quantum chemistry to combinatorial …

This paper explores the potential benefits of quantum coherence and quantum discord in the
non-universal quantum computing model called deterministic quantum computing with one …

Quantum kernel methods are promising candidates for achieving a practical quantum
advantage for certain machine learning tasks. Similar to classical machine learning, an …

Quantum machine learning endeavors to exploit quantum mechanical effects like
superposition, entanglement, and interference to enhance the capabilities of classical …

Exploiting the properties of quantum information to the benefit of machine learning models is
perhaps the most active field of research in quantum computation. This interest has …