2023/01/18 by Xiongzhi Zeng, Yi Fan, Zeng, Xiongzhi +7 · 2 citations
Computer Science · #Chemical Physics (physics.chem-ph) #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci) #Neural Networks and Reservoir Computing #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Physics (quant-ph)
paper · pdf · doi:10.48550/arxiv.2301.07542
openalex publication_date 2023/01/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The variational quantum eigensolver is a promising way to solve the Schrödinger 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.