We investigate the coherence measures induced by fidelity and trace norm, based on the coherence quantification recently proposed by Baumgratz et al. [T. Baumgratz, M. Cramer, and M. B. Plenio, Phys. Rev. Lett. 113, 140401 (2014)]. We show that the fidelity of coherence does not in general satisfy the monotonicity requirement as a measure of coherence under the subselection of the measurement condition. We find that the trace norm of coherence can act as a measure of coherence for qubits and some special class of qutrits with some restrictions on the incoherent operators, while the general case needs to be explored further.

We investigate the quantification of coherence in the infinite-dimensional systems, and especially, we focus on the infinite-dimensional bosonic systems in the Fock space. We find that given the average energy constraints, the relative entropy of coherence serves as a well-defined quantification of coherence in the infinite-dimensional systems, however, the $l_1$ norm of coherence fails. Via using the relative entropy of coherence as the quantification of coherence, we generalize the case to multimode Fock spaces, and some special examples are considered. It is shown that with a finite average particle number, increasing the number of modes of light can enhance the relative entropy of coherence. With the mean energy constraint, our results can also be extended to other infinite-dimensional systems.

Quantum coherence with respect to orthonormal bases has been studied extensively in the past few years. Recently, Bischof, Kampermann, and Bruß [ Phys. Rev. Lett. 123, 110402 (2019)] generalized it to the case of general positive operator-valued measure (POVM) measurements. Such POVM-based coherence, including the block coherence as a special case, have significant operational interpretations in quantifying the advantage of quantum states in quantum information processing. In this work we first establish an alternative framework for quantifying the block coherence and provide several block coherence measures. We then present several coherence measures with respect to POVM measurements, and prove a conjecture on the $l_1$-norm related POVM coherence measure.

Defect detection is an indispensable part of the industrial intelligence process. The introduction of the DETR model marked the successful application of a transformer for defect detection, achieving true end-to-end detection. However, due to the complexity of defective backgrounds, low resolutions can lead to a lack of image detail control and slow convergence of the DETR model. To address these issues, we proposed a defect detection method based on an improved DETR model, called the GM-DETR. We optimized the DETR model by integrating GAM global attention with CNN feature extraction and matching features. This optimization process reduces the defect information diffusion and enhances the global feature interaction, improving the neural network’s performance and ability to recognize target defects in complex backgrounds. Next, to filter out unnecessary model parameters, we proposed a layer pruning strategy to optimize the decoding layer, thereby reducing the model’s parameter count. In addition, to address the issue of poor sensitivity of the original loss function to small differences in defect targets, we replaced the L1 loss in the original loss function with MSE loss to accelerate the network’s convergence speed and improve the model’s recognition accuracy. We conducted experiments on a dataset of road pothole defects to further validate the effectiveness of the GM-DETR model. The results demonstrate that the improved model exhibits better performance, with an increase in average precision of 4.9% (mAP@0.5), while reducing the parameter count by 12.9%.

Sentiment analysis aims to study, analyse and identify the sentiment polarity contained in subjective documents. In the realm of natural language processing (NLP), the study of sentiment analysis and its subtask research is a hot topic, which has very important significance. The existing sentiment analysis methods based on sentiment lexicon and machine learning take into account contextual semantic information, but these methods still lack the ability to utilize context information, so they cannot effectively encode context information. Inspired by the concept of density matrix in quantum mechanics, we propose a sentiment analysis method, named Complex-valued Quantum-enhanced Long Short-term Memory Neural Network (CQLSTM). It leverages complex-valued embedding to incorporate more semantic information and utilizes the Complex-valued Quantum-enhanced Long Short-term Memory Neural Network for feature extraction. Specifically, a complex-valued neural network based on density matrix is used to capture interactions between words (i.e., the correlation between words). Additionally, the Complex-valued Quantum-enhanced Long Short-term Memory Neural Network, which is inspired by the quantum measurement theory and quantum long short-term memory neural network, is developed to learn interactions between sentences (i.e., contextual semantic information). This approach effectively encodes semantic dependencies, which reflects the dispersion of words in the embedded space of sentences and comprehensively captures interactive information and long-term dependencies among the emotional features between words. Comparative experiments were performed on four sentiment analysis datasets using five traditional models, showcasing the effectiveness of the CQLSTM model.

Kidney cancer is a malignant tumor with a high mortality rate. The accurate segmentation of tumors from computed tomography (CT) scans can assist physicians in clinical diagnosis. We introduced a new segmentation network called DWR-SegFormer to address the challenge of accurately segmenting kidney tumors in CT images. The method involved binarizing the label maps of clear cell renal cell carcinoma and papillary renal cell carcinoma CT images for identification, and the cancer lesion area was obtained by the label so that the model could accurately identify the area and enhance the feature extraction ability. Secondly, an optimized segmentation model combining a DWR attention mechanism and SegFormer network was constructed. MiT-B0 was used as the encoder of the model to establish long-distance feature dependencies and effectively extract feature information at different resolutions. The decoder with a multi-branch DWR module was implemented to utilize multi-scale feature information effectively and enhance segmentation accuracy. Comparing the experimental results with other existing models shows that the model significantly outperformed the comparison methods in all evaluation metrics on the CT image dataset of clear cell renal cancer. Furthermore, the experimental findings highlight the robustness of the proposed model across other datasets.