For quantum computers to successfully solve real-world problems, it is necessary to tackle
the challenge of noise: the errors that occur in elementary physical components due to …

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 …

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

A universal fault-tolerant quantum computer that can efficiently solve problems such as
integer factorization and unstructured database search requires millions of qubits with low …

BACKGROUND The past two decades have seen intense efforts aimed at building quantum
computing hardware with the potential to solve problems that are intractable on classical …

Applications such as simulating complicated quantum systems or solving large-scale linear
algebra problems are very challenging for classical computers, owing to the extremely high …

It is for the first time that quantum simulation for high-energy physics (HEP) is studied in the
US decadal particle-physics community planning, and in fact until recently, this was not …

Parametrized quantum circuits serve as ansatze for solving variational problems and
provide a flexible paradigm for the programming of near-term quantum computers. Ideally …

The use of quantum computing for machine learning is among the most exciting prospective
applications of quantum technologies. However, machine learning tasks where data is …

Variational quantum machine learning is an extensively studied application of near-term
quantum computers. The success of variational quantum learning models crucially depends …