  Variational quantum eigensolvers (VQEs) are one of the most important and
effective applications of quantum computing, especially in the current noisy
intermediate-scale quantum (NISQ) era. There are mainly two ways for VQEs:
problem-agnostic and problem-specific. For problem-agnostic methods, they often
suffer from trainability issues. For problem-specific methods, their
performance usually relies upon choices of initial reference states which are
often hard to determine. In this paper, we propose an Entanglement-variational
Hardware-efficient Ansatz (EHA), and numerically compare it with some widely
used ansatzes by solving benchmark problems in quantum many-body systems and
quantum chemistry. Our EHA is problem-agnostic and hardware-efficient,
especially suitable for NISQ devices and having potential for wide
applications. EHA can achieve a higher level of accuracy in finding ground
states and their energies in most cases even compared with problem-specific
methods. The performance of EHA is robust to choices of initial states and
parameters initialization and it has the ability to quickly adjust the
entanglement to the required amount, which is also the fundamental reason for
its superiority.
