Robust resource-efficient quantum variational ansatz through an evolutionary algorithm

Huang, Yuhan1; Li, Qingyu1; Hou, Xiaokai1; Wu, Rebing2; Yung, Man-Hong3,4,5; Bayat, Abolfazl1; Wang, Xiaoting1

2022-05-10

10.1103/PhysRevA.105.052414

PHYSICAL REVIEW A   影响因子和分区

2469-9926

2469-9934

Variational quantum algorithms (VQAs) are promising methods to demonstrate quantum advantage on near term devices as the required resources are divided between a quantum simulator and a classical optimizer. As such, designing a VQA which is resource-efficient and robust against noise is a key factor to achieve a potential advantage with the existing noisy quantum simulators. It turns out that a fixed VQA circuit design, such as the widely used hardware-efficient ansatz, is not necessarily robust against imperfections. In this work, we propose a genome-length-adjustable evolutionary algorithm to design a robust VQA circuit that is optimized over variations of both circuit ansatz and gate parameters, without any prior assumptions on circuit structure or depth. Remarkably, our method not only generates a noise-effect-minimized circuit with shallow depth, but also accelerates the classical optimization by substantially reducing the number of parameters. In this regard, the optimized circuit is far more resource-efficient with respect to both quantum and classical resources. As applications, based on two typical error models in VQA, we apply our method to calculate the ground energy of the hydrogen and the water molecules as well as the Heisenberg model. Simulations suggest that, compared with conventional hardware-efficient ansatz, our circuit-structure-tunable method can generate circuits apparently more robust against both coherent and incoherent noise and hence is more likely to be implemented on near-term devices.

SCI ; EI

英语

其他

National Key R&D Program of China[2018YFA0306703] ; National Science Foundation of China[61833010,61773232] ; Ministry of Science and Technology of China[QNJ2021167001L]

Optics ; Physics

Optics ; Physics, Atomic, Molecular & Chemical

WOS:000798848000006

AMER PHYSICAL SOC

20222112137706

Evolutionary algorithms ; Molecules ; Quantum efficiency ; Quantum theory ; Structural design

Structural Design, General:408.1 ; Semiconductor Devices and Integrated Circuits:714.2 ; Atomic and Molecular Physics:931.3 ; Quantum Theory; Quantum Mechanics:931.4

PHYSICS

Web of Science

1.Univ Elect Sci & Technol China, Inst Fundamental & Frontier Sci, Chengdu 610051, Peoples R China
2.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
3.Huawei Technol, Cent Res Inst, 2012 Labs, Shenzhen 518129, Peoples R China
4.Southern Univ Sci & Technol, Dept Phys, Shenzhen 518055, Peoples R China
5.Southern Univ Sci & Technol, Shenzhen Inst Quantum Sci & Engn, Shenzhen 518055, Peoples R China

Huang, Yuhan,Li, Qingyu,Hou, Xiaokai,et al. Robust resource-efficient quantum variational ansatz through an evolutionary algorithm[J]. PHYSICAL REVIEW A,2022,105(5).

Huang, Yuhan.,Li, Qingyu.,Hou, Xiaokai.,Wu, Rebing.,Yung, Man-Hong.,...&Wang, Xiaoting.(2022).Robust resource-efficient quantum variational ansatz through an evolutionary algorithm.PHYSICAL REVIEW A,105(5).

Huang, Yuhan,et al."Robust resource-efficient quantum variational ansatz through an evolutionary algorithm".PHYSICAL REVIEW A 105.5(2022).