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Variational quantum algorithms (VQAs) are promising methods to demonstratequantum advantage on near-term devices as the required resources are dividedbetween a quantum simulator and a classical optimizer. As such, designing a VQAwhich is resource-efficient and robust against noise is a key factor to achievepotential advantage with the existing noisy quantum simulators. It turns outthat a fixed VQA circuit design, such as the widely-used hardware efficientansatz, is not necessarily robust against imperfections. In this work, wepropose a genome-length-adjustable evolutionary algorithm to design a robustVQA circuit that is optimized over variations of both circuit ansatz and gateparameters, without any prior assumptions on circuit structure or depth.Remarkably, our method not only generates a noise-effect-minimized circuit withshallow depth, but also accelerates the classical optimization by substantiallyreducing the number of parameters. In this regard, the optimized circuit is farmore resource-efficient with respect to both quantum and classical resources.As applications, based on two typical error models in VQA, we apply our methodto calculate the ground energy of the hydrogen and the water molecules as wellas the Heisenberg model. Simulations suggest that compared with conventionalhardware efficient ansatz, our circuit-structure-tunable method can generatecircuits apparently more robust against both coherent and incoherent noise, andhence is more likely to be implemented on near-term devices.

Robust resource-efficient quantum variational ansatz through evolutionary algorithm