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Quantum error correction (QEC) is a key technique for building scalable quantum computers that can be used to mitigate the effects of errors on physical quantum bits. Since quantum states are more or less affected by noise, errors are inevitable. Traditional QEC codes face huge challenges. Therefore, designing an error suppression algorithm based on neural networks (NN) and quantum topological error correction (QTEC) codes is particularly important for quantum teleportation. In this paper, QTEC codes: semion codes—a greater than 2 dimensional (2D) error correction code based on the double semion model—are used to suppress errors during quantum teleportation, using a NN to build a decoder based on semion codes and to simulate the quantum information error suppression process and the suppression effect. The proposed convolutional neural network (CNN) decoder is suitable for small distance topological semion codes. The aim is to optimize the NN for better decoder performance while deriving the relationship between decoder performance and slope and pseudothreshold during training and calculate the thresholds for different noise areas when the code distances are the same, P t h r e s h o l d = 0.082 for A r e a < 0.007   d B and P t h r e s h o l d = 0.096 for A r e a < 0.01   d B . This paper demonstrates the ability of CNNs to suppress errors in quantum transmission information and the great potential of NNs in the field of quantum computing.

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Boson quantum error correction is an important means to realize quantum error correction information processing. In this paper, we consider the connection of a single-mode Gottesman-Kitaev-Preskill (GKP) code with a two-dimensional (2D) surface (surface-GKP code) on a triangular quadrilateral lattice. On the one hand, we use a Steane-type scheme with maximum likelihood estimation for surface-GKP code error correction. On the other hand, the minimum-weight perfect matching (MWPM) algorithm is used to decode surface-GKP codes. In the case where only the data GKP qubits are noisy, the threshold reaches σ ≈ 0.5 ($$\bar{p}\approx 12.3 \%$$ p ¯ ≈ 12.3 % ). If the measurement is also noisy, the threshold is reached σ ≈ 0.25 ($$\bar{p}\approx 10.02 \%$$ p ¯ ≈ 10.02 % ). More importantly, we introduce a neural network decoder. When the measurements in GKP error correction are noise-free, the threshold reaches σ ≈ 0.78 ($$\bar{p}\approx 15.12 \%$$ p ¯ ≈ 15.12 % ). The threshold reaches σ ≈ 0.34 ($$\bar{p}\approx 11.37 \%$$ p ¯ ≈ 11.37 % ) when all measurements are noisy. Through the above optimization method, multi-party quantum error correction will achieve a better guarantee effect in fault-tolerant quantum computing.

Phantoms simulating polarization characteristics of soft tissue play an important role in the development, calibration, and validation of diagnostic polarized imaging devices and of therapeutic strategy, in both laboratory and clinical settings. We propose to fabricate optical phantoms that simulate polarization characteristics of dense fibrous tissues by bonding electrospun polylactic acid (PLA) fibers between polydimethylsiloxane (PDMS) substrate with a groove. Increasing the rotational speed of an electrospinning collector helps improve the orientation of the electrospun fibers. The phantoms simulate the polarization characteristics of dense fibrous tissue of collagenous fibroma and healthy skin with high fidelity. Our experiments demonstrate the technical potential of using such phantoms for validation and calibration of polarimetric medical devices.

To develop and evaluate the clinical application of a multimodal colposcopy combining multispectral reflectance, autofluorescence, and red, green, blue (RGB) imaging for noninvasive characterization of cervical intraepithelial neoplasia (CIN). We developed a multimodal colposcopy system that combined multispectral reflectance, autofluorescence, and RGB imaging for noninvasive characterization of CIN. We studied the optical properties of cervical tissue first; then the imaging system was designed and tested in a clinical trial where comprehensive datasets were acquired and analyzed to differentiate between squamous normal and high grade types of cervical tissue. The custom-designed multimodal colposcopy is capable of acquiring multispectral reflectance images, autofluorescence images, and RGB images of cervical tissue consecutively. The classification algorithm was employed on both normal and abnormal cases for image segmentation. The performance characteristics of this system were comparable to the gold standard histopathologic measurements with statistical significance. Our pilot study demonstrated the clinical potential of this multimodal colposcopic system for noninvasive characterization of CIN. The proposed system was simple, noninvasive, cost-effective, and portable, making it a suitable device for deployment in developing countries or rural regions of limited resources.

Understanding gene functions and their associated abnormal phenotypes is crucial in the prevention, diagnosis and treatment against diseases. The Human Phenotype Ontology (HPO) is a standardized vocabulary for describing the phenotype abnormalities associated with human diseases. However, the current HPO annotations are far from completion, and only a small fraction of human protein-coding genes has HPO annotations. Thus, it is necessary to predict protein-phenotype associations using computational methods. Protein sequences can indicate the structure and function of the proteins, and interacting proteins are more likely to have same function. It is promising to integrate these features for predicting HPO annotations of human protein. We developed GraphPheno, a semi-supervised method based on graph autoencoders, which does not require feature engineering to capture deep features from protein sequences, while also taking into account the topological properties in the protein–protein interaction network to predict the relationships between human genes/proteins and abnormal phenotypes. Cross validation and independent dataset tests show that GraphPheno has satisfactory prediction performance. The algorithm is further confirmed on automatic HPO annotation for no-knowledge proteins under the benchmark of the second Critical Assessment of Functional Annotation, 2013–2014 (CAFA2), where GraphPheno surpasses most existing methods. Further bioinformatics analysis shows that predicted certain phenotype-associated genes using GraphPheno share similar biological properties with known ones. In a case study on the phenotype of abnormality of mitochondrial respiratory chain, top prioritized genes are validated by recent papers. We believe that GraphPheno will help to reveal more associations between genes and phenotypes, and contribute to the discovery of drug targets.

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