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