Abstract Machine learning has been used in high energy physics (HEP) for a long time,
primarily at the analysis level with supervised classification. Quantum computing was …

Quantum technologies have become powerful tools for a wide range of application
disciplines, which tend to range from chemistry to agriculture, natural language processing …

We propose a quantum algorithm to solve systems of nonlinear differential equations. Using
a quantum feature map encoding, we define functions as expectation values of parametrized …

This article presents a first end-to-end application of a quantum support vector machine
(QSVM) algorithm for a classification problem in the financial payment industry using the IBM …

Quantum entanglement is one of the essential resources involved in quantum information
processing tasks. However, its detection for usage remains a challenge. The Bell-type …

Over the last few decades, quantum machine learning has emerged as a groundbreaking
discipline. Harnessing the peculiarities of quantum computation for machine learning tasks …

Generative adversarial networks (GANs) are a machine learning framework comprising a
generative model for sampling from a target distribution and a discriminative model for …

Quantum machine learning has emerged as an exciting and promising paradigm inside
quantum technologies. It may permit, on the one hand, to carry out more efficient machine …

We propose a new technique for the automatic generation of optimal ad-hoc ansätze for
classification by using quantum support vector machine. This efficient method is based on …

Reliable quantum supervised learning of a multivariate function mapping depends on the
expressivity of the corresponding quantum circuit and measurement resources. We …