Quantum Neural Network Inspired Hardware Adaptable Ansatz for Efficient Quantum Simulation of Chemical Systems

TL;DR

This paper introduces a quantum neural network-inspired hardware heuristic ansatz that reduces circuit depth and enhances adaptability, enabling more practical quantum simulations of chemical systems on current NISQ devices.

Contribution

It proposes a novel ansatz that leverages ancilla qubits to lower circuit depth and improve expressibility, advancing quantum simulation capabilities for NISQ hardware.

Findings

Enables simulation of chemical reactions with over 20 atoms on existing quantum computers.

Reduces circuit depth while maintaining expressibility through ancilla qubits.

Provides a flexible ansatz adaptable to various hardware constraints.

Abstract

The variational quantum eigensolver is a promising way to solve the Schr\"odinger equation on a noisy intermediate-scale quantum (NISQ) computer, while its success relies on a well-designed wavefunction ansatz. Compared to physically motivated ansatzes, hardware heuristic ansatzes usually lead to a shallower circuit, but it may still be too deep for an NISQ device. Inspired by the quantum neural network, we propose a new hardware heuristic ansatz where the circuit depth can be significantly reduced by introducing ancilla qubits, which makes a practical simulation of a chemical reaction with more than 20 atoms feasible on a currently available quantum computer. More importantly, the expressibility of this new ansatz can be improved by increasing either the depth or the width of the circuit, which makes it adaptable to different hardware environments. These results open a new avenue to develop practical applications of quantum computation in the NISQ era.