The variational quantum eigensolver (or VQE), first developed by Peruzzo et al.(2014), has
received significant attention from the research community in recent years. It uses the …

Quantum random sampling is the leading proposal for demonstrating a computational
advantage of quantum computers over classical computers. Recently the first large-scale …

We study the problem of generating independent samples from the output distribution of
Google's Sycamore quantum circuits with a target fidelity, which is believed to be beyond the …

Quantum computing holds substantial potential for applications in biology and medicine,
spanning from the simulation of biomolecules to machine learning methods for subtyping …

Recent advances in quantum computing devices have brought attention to hybrid quantum-
classical algorithms like the variational quantum eigensolver (VQE) as a potential route to …

Demonstrating quantum advantage requires experimental implementation of a
computational task that is hard to achieve using state-of-the-art classical systems. One …

The biggest challenge that quantum computing and quantum machine learning are currently
facing is the presence of noise in quantum devices. As a result, big efforts have been put into …

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 …

Quantum state preparation is a vital routine in many quantum algorithms, including solution
of linear systems of equations, Monte Carlo simulations, quantum sampling, and machine …

The quantum advantage threshold determines when a quantum processing unit (QPU) is
more efficient with respect to classical computing hardware in terms of algorithmic …