Variational quantum algorithms are the leading candidate for advantage on near-term
quantum hardware. When training a parametrized quantum circuit in this setting to solve a …

Using a quantum processor to embed and process classical data enables the generation of
correlations between variables that are inefficient to represent through classical …

We investigate quantum circuits for graph representation learning, and propose equivariant
quantum graph circuits (EQGCs), as a class of parameterized quantum circuits with strong …

We develop quantum protocols for anomaly detection and apply them to the task of credit
card fraud detection (FD). First, we establish classical benchmarks based on supervised and …

Many applications of quantum computing in the near term rely on variational quantum
circuits (VQCs). They have been showcased as a promising model for reaching a quantum …

The application of quantum computation to accelerate machine learning algorithms is one of
the most promising areas of research in quantum algorithms. In this paper, we explore the …

Machine learning models capable of handling the large data sets collected in the financial
world can often become black boxes expensive to run. The quantum computing paradigm …

Abstract Graph Neural Networks (GNNs) and Graph Kernels (GKs) are two fundamental
tools used to analyze graph-structured data. Efforts have been recently made in developing …

Learn the principles of quantum machine learning and how to apply them While focus is on
financial use cases, all the methods and techniques are transferable to other fields Purchase …

Parameterized quantum circuits as machine learning models are typically well described by
their representation as a partial Fourier series of the input features, with frequencies …