Abstract The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising
variational quantum algorithm that aims to solve combinatorial optimization problems that …

During the last ten years, superconducting circuits have passed from being interesting
physical devices to becoming contenders for near-future useful and scalable quantum …

A large amount of effort has recently been put into understanding the barren plateau
phenomenon. In this perspective article, we face the increasingly loud elephant in the room …

We develop a density-matrix renormalization group (DMRG) algorithm for the simulation of
quantum circuits. This algorithm can be seen as the extension of the time-dependent DMRG …

Many quantum algorithms seek to output a specific bitstring solving the problem of interest—
or a few if the solution is degenerate. It is the case for the quantum approximate optimization …

The trading of fidelity for scale enables approximate classical simulators such as matrix
product states (MPSs) to run quantum circuits beyond exact methods. A control parameter …

Here, we focus on the problem of minimizing complex classical cost functions associated
with prototypical discrete neural networks, specifically the paradigmatic Hopfield model and …

The performance of the Variational Quantum Eigen-solver (VQE) is promising compared to
other quantum algorithms, but also depends significantly on the appropriate design of the …

In this paper, we employ PCA and t-SNE analyses to gain deeper insights into the behavior
of entangled and non-entangled mixing operators within the Quantum Approximate …

The ability to generate bipartite entanglement in quantum computing technologies is widely
regarded as pivotal. However, the role of genuinely multipartite entanglement is much less …