EHA: Entanglement-variational Hardware-efficient Ansatz for Eigensolvers

Abstract

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.

18 pages, 23 figures

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• Trainability Enhancement of Parameterized Quantum Circuits via Reduced-Domain Parameter Initialization
• Machine-Learning Insights into the Entanglement-trainability Correlation of Parameterized Quantum Circuits
• Supervised Learning Guarantee for Quantum AdaBoost
• Tackling the Challenges of Adding Pulse-level Support to a Heterogeneous HPCQC Software Stack: MQSS Pulse
• Exploiting many-body localization for scalable variational quantum simulation