The objective to be minimized in the variational quantum eigensolver (VQE) has a restricted form, which allows a specialized sequential minimal optimization (SMO) that requires only a few observations in each iteration. However, the SMO iteration is still costly due to the observation noise—one observation at a point typically requires averaging over hundreds to thousands of repeated quantum measurement shots for achieving a reasonable noise level. In this paper, we propose an adaptive cost control method, named subspace in confident region (SubsCoRe), for SMO. SubsCoRe uses the Gaussian process (GP) surrogate, and requires it to have low uncertainty over the subspace being updated, so that optimization in each iteration is performed with guaranteed accuracy. Adaptive cost control is performed by setting the required accuracy according to the progress of the optimization, and identifying the minimum number of measurement shots, as well as their distribution, satisfying the SubsCoRe requirement.

Automatic code review (ACR), aiming to relieve manual inspection costs, is an indispensable and essential task in software engineering.The existing works only use the source code fragments to predict the results, missing the exploitation of developer's comments.Thus, we present a Multi-Modal Apache Automatic Code Review dataset (MACR) for the Multi-Modal ACR task.The release of this dataset would push forward the research in this field.Based on it, we propose a Contrastive Learning based Multi-Modal Network (CLMN) to deal with the Multi-Modal ACR task.Concretely, our model consists of a code encoding module and a text encoding module.For each module, we use the dropout operation as minimal data augmentation.Then, the contrastive learning method is adopted to pre-train the module parameters.Finally, we combine the two encoders to fine-tune the CLMN to decide the results of Multi-Modal ACR.Experimental results on the MACR dataset illustrate that our proposed model outperforms the state-of-the-art methods.