The Quantum Internet, by enabling quantum communications among remote quantum
nodes, is a network capable of supporting functionalities with no direct counterpart in the …

Variational quantum algorithms are believed to be promising for solving computationally
hard problems on noisy intermediate-scale quantum (NISQ) systems. Gaining computational …

We introduce Quantum Graph Neural Networks (QGNN), a new class of quantum neural
network ansatze which are tailored to represent quantum processes which have a graph …

Approximate combinatorial optimisation has emerged as one of the most promising
application areas for quantum computers, particularly those in the near term. In this work, we …

We introduce a new class of generative quantum-neural-network-based models called
Quantum Hamiltonian-Based Models (QHBMs). In doing so, we establish a paradigmatic …

One of the open challenges in quantum computing is to find meaningful and practical
methods to leverage quantum computation to accelerate classical machine-learning …

The quantum approximate optimization algorithm (QAOA), as a hybrid quantum/classical
algorithm, has received much interest recently. QAOA can also be viewed as a variational …

The use of variational quantum algorithms for optimization tasks has emerged as a crucial
application for the current noisy intermediate-scale quantum computers. However, these …

We calculate the energy levels and corresponding eigenstates of an interacting scalar
quantum field theory on a lattice using a continuous-variable version of the quantum …

The availability of large-scale datasets on which to train, benchmark and test algorithms has
been central to the rapid development of machine learning as a discipline. Despite …