Variational quantum algorithms, as one of the most promising routes in the noisy intermediate-scale quantum era, offer various potential applications while also confronting severe challenges due to near-term quantum hardware restrictions. In this work, we propose a framework to enhance the expressiveness of a variational quantum ansatz by incorporating variational post-selection techniques. These techniques apply variational modules and neural network post-processing on ancilla qubits, which are compatible with the current generation of quantum devices. Equipped with variational post-selection, we demonstrate that the accuracy of the variational ground state and thermal state preparation for both quantum spin and molecule systems is substantially improved. Notably, in the case of estimating the local properties of a thermalized quantum system, we present a scalable approach that outperforms previous methods through the combination of neural post-selection and a new optimization objective.

中文翻译：

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基态和热态仿真的变分后选


变分量子算法作为嘈杂的中尺度量子时代最有前途的路线之一，提供了各种潜在的应用，同时由于近期量子硬件的限制也面临着严峻的挑战。在这项工作中，我们提出了一个框架，通过结合变分后选择技术来增强变分量子拟设的表达能力。这些技术在辅助量子比特上应用变分模块和神经网络后处理，这与当前一代量子设备兼容。配备变分后选择，我们证明了量子自旋和分子系统的变分基态和热态制备的准确性都得到了显著提高。值得注意的是，在估计热化量子系统的局部特性的情况下，我们提出了一种可扩展的方法，该方法通过结合神经后选择和新的优化目标，其性能优于以前的方法。