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

We propose a hybrid classical-quantum digitized counterdiabatic algorithm to tackle the
protein-folding problem on a tetrahedral lattice. Digitized counterdiabatic quantum …

The quantum approximate optimization algorithm (QAOA) has proved to be an effective
classical-quantum algorithm serving multiple purposes, from solving combinatorial …

We consider digitized-counterdiabatic quantum computing as an advanced paradigm to
approach quantum advantage for industrial applications in the NISQ era. We apply this …

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

We develop a hardware-efficient ansatz for variational optimization, derived from existing
ansatzes in the literature, that parametrizes subsets of all interactions in the cost Hamiltonian …

Abstract Noisy Intermediate-Scale Quantum (NISQ) devices lack error correction, limiting
scalability for quantum algorithms. In this context, digital-analog quantum computing (DAQC) …

The prospect of achieving computational speedups by exploiting quantum phenomena
makes the use of quantum processing units (QPUs) attractive for many algorithmic database …

Quantum alternating operator ansatz (QAOA) has a strong connection to the adiabatic
algorithm, which it can approximate with sufficient depth. However, it is unclear to what …

Quantum variational optimization has been posed as an alternative to solve optimization
problems faster and at a larger scale than what classical methods allow. In this paper we …