Single entanglement connection architecture between multi-layer bipartite Hardware Efficient Ansatz

TL;DR

This paper introduces SECA, a novel entanglement architecture for bipartite hardware efficient ansatzes, improving quantum algorithm performance and scalability in NISQ devices.

Contribution

Proposes SECA, a new entanglement architecture that enhances expressibility, entangling capability, and trainability of variational quantum circuits.

Findings

SECA outperforms FECA in computational tasks.

Combining SECA with gate-cutting enables scalable distributed quantum computation.

SECA improves the effectiveness of training circuits in quantum algorithms.

Abstract

Variational quantum algorithms (VQAs) are among the most promising algorithms to achieve quantum advantages in the NISQ era. One important challenge in implementing such algorithms is to construct an effective parameterized quantum circuit (also called an ansatz). In this work, we propose a single entanglement connection architecture (SECA) for a bipartite hardware efficient ansatz (HEA) by balancing its expressibility, entangling capability, and trainability. Numerical simulations with a one-dimensional Heisenberg model and quadratic unconstrained binary optimization (QUBO) issues were conducted. Our results indicate the superiority of SECA over the common full entanglement connection architecture (FECA) in terms of computational performance. Furthermore, combining SECA with gate-cutting technology to construct distributed quantum computation (DQC) can efficiently expand the size of NISQ devices under low overhead. We also demonstrated the effectiveness and scalability of the DQC scheme. Our study is a useful indication for understanding the characteristics associated with an effective training circuit.