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

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 …

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 …

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 …

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 …

One of the fastest growing areas of interest in quantum computing is its use within machine
learning methods, in particular through the application of quantum kernels. Despite this …

In the current quantum computing paradigm, significant focus is placed on the reduction or
mitigation of quantum decoherence. When designing new quantum processing units, the …

Quantum kernel methods leverage a kernel function computed by embedding input
information into the Hilbert space of a quantum system. However, large Hilbert spaces can …