Reviewerly/exHarmony huggingface dataset:
Towards a Dutch Hybrid Quantum/HPC Infrastructure

Quantum Inspire has taken important steps to enable quantum applications by developing a setting that allows the execution of hybrid algorithms. Currently, the setting uses a classical server (HPC node) co-located with the quantum computer for the high frequency coupling needed by hybrid algorithms. A fast task manager (dispatcher) has been developed to orchestrate the interaction between the server and the quantum computer. Although successful, the setting imposes a specific hybrid job-structure. This is most likely always going to be the case and we are currently discussing how to make sure this does not hamper the uptake of the setting. Furthermore, first steps have been taken towards the integration with the Dutch National High-Performance Computing (HPC) Center, hosted by SURF. As a first approach we have setup a setting consisting of two SLURM clusters, one in the HPC (C1) and the second (C2) co-located with Quantum Inspire API. Jobs are submitted from C1 to C2. Quantum Inspire can then schedule with C2 the jobs to the quantum computer. With this setting, we enable control from both SURF and Quantum Inspire on the jobs being executed. By using C1 for the jobs submission we remove the accounting burden from Quantum Inspire. By having C2 co-located with Quantum Inspire API, we make the setting more resilient towards network failures. This setting can be extended for other HPC centers to submit jobs to Quantum Inspire backends.
Scalable Quantum Simulation for Topological Phases on NISQ Devices

Topological quantum phases emerge from correlated quantum many-body systems containing novel features such as nontrivial entanglement structure and mutual statistics. However, the co-emerged exponential computational complexity strongly hampers the research of these systems using classical computers. Present booming quantum computing techniques offer a new way to investigate these challenging systems: the quantum simulation approach. Combining current available noise intermediate-scale quantum (NISQ) devices with variational quantum eigensolver (VQE) algorithm to solve quantum many-body problems has attracted extensive attention. Although the NISQ devices have up to hundreds of qubits, treating a quantum many-body problem with a similar size on these devices is still impossible. One fundamental reason for this is the lack of scalability in current quantum algorithms (i.e., the required quantum resources for realizing a quantum algorithm increase much faster than the increase of the problem size). In this poster, we introduce how to realize scalable VQE calculation in the topological quantum phases by designing problem-specified scalable parameterized quantum circuits (PQC). We construct a PQC with a two-layer structure to capture both the basic entanglement structure of the target topological phase and finite correlations governed by the details of the many-body Hamiltonian. Utilizing such a PQC, we numerically simulate both symmetry-protected topological (SPT) Haldane phase and trivial dimmer phase in the non-exactly solvable alternating Heisenberg chain using the VQE algorithm. Furthermore, we realize these optimized circuits on real quantum computers provided by IBM Quantum and observe the main characterizations of the SPT Haldane phase.
Reliable Devices Yield Stable Quantum Computations

Stable quantum computation requires noisy results to remain bounded even in the presence of noise fluctuations. Yet non-stationary noise processes lead to drift in the varying characteristics of a quantum device that can greatly influence the circuit outcomes. Here we address how temporal and spatial variations in noise relate device reliability to quantum computing stability. First, our approach quantifies the differences in statistical distributions of characterization metrics collected at different times and locations using Hellinger distance. We then validate an analytical bound that relates this distance directly to the stability of a computed expectation value. Our demonstration uses numerical simulations with models informed by the washington superconducting transmon device. We find that the stability metric is consistently bounded from above by the corresponding Hellinger distance, which can be cast as a specified tolerance level. These results underscore the significance of reliable quantum computing devices and the impact for stable quantum computation.
Exploring Hybrid Classical-Quantum Compute Systems through Simulation

The computational requirements of applications running in supercomputing centres are growing rapidly and diversifying over time. To meet these needs, developers are constantly developing new processors, domain-specific accelerators, and technologies. Quantum computing is one notable technology that has the potential to speed up certain computations and could be used to enhance High Performance Computing (HPC). However, integrating a quantum computer (QC) into HPC systems is a complex and challenging task, both at the physical and software level. Although various types of accelerators have been integrated into HPC in the past, the integration of QC into HPC presents a unique challenge as it operates on a new computing paradigm and is a scarce resource that needs to be shared among multiple jobs. Furthermore, HPCQC integration is a multidisciplinary field and integration can mean different things to different stakeholders. Our approach to HPCQC integration is from the perspective of an HPC centre where QC is integrated into the HPC environment as an accelerator. We identify parameters that are important for the integration, including network latency, HPCQC hybrid application characteristics and fine-grained scheduling. In addition, we develop a custom simulation model that can comprehensively simulate a hybrid classical-quantum compute system at selected level of detail. This simulation model is utilised to simulate the performance of a hybrid application by adjusting its features on a hybrid system with varying parameters. We discover that fine-grained scheduling is critical to performance, and network latency between QC and HPC has a significant impact on hybrid system performance.
Understanding the Effect of Transpilation in the Reliability of Quantum Circuits

Transpiling is a necessary step to map a logical quantum algorithm to a circuit executed on a physical quantum machine, according to the available gate set and connectivity topology. Different transpiling approaches try to minimize the most critical parameters for the current transmon technology, such as Depth and CNOT number. Crucially, these approaches do not take into account the reliability of the circuit. In particular, transpilation can modify how radiation-induced transient faults propagate. In this paper, we aim at advancing the understanding of transpilation impact on fault propagation by investigating the low-level reliability of several transpiling approaches. We considered 4 quantum algorithms transpiled for 2 different architectures, increasing the number of qubits, and all possible logical-to-physical qubit mapping, adding to a total of 4, 640 transpiled circuits. We inject a total of 202, 124 faults and track their propagation. Our experiments show that by simply choosing the proper transpilation, the reliability of the circuit can improve by up to 14%.
A Cube Workpiece Positioning Method Based on Point Structured Light Scanning

In the automation upgrading process of traditional labor-intensive industries, positioning method of workpiece affects the automated operation quality, efficiency, and system cost directly. Traditional workpiece positioning methods based on image vision and line structured light usually require higher professional knowledge or cost, which increase the difficulty for widely application. Therefore, this paper proposes a workpiece positioning method based on point structured light scanning for cube-shaped workpieces. Positioning accuracy is analyzed and the feasibility of this method is verified through numerical example. The result of this research provides a precise and low-cost workpiece positioning method for the automation upgrade of traditional labor-intensive industries.
The Impact of Quantum Memory Quality on Entanglement Assisted Communication

This work explores entanglement-assisted communication, where quantum entanglement resources enable the transmission of classical information at an enhanced rate. We consider a scenario where entanglement is distributed ahead of time based on network traffic levels, and simulate a setting where idle nodes generate and store entanglement to later transmit messages at an accelerated rate. We analyze this communication model using noise models for quantum memory in various scenarios, and extend our investigation to a quantum-enhanced distributed computing environment, where entanglement storage enhances data transmission rates for cooperative data processing. We propose a protocol and demonstrate a distributed version of unsupervised clustering. Our results show that, for qubit channels, high rates of entanglement generation and modest storage requirements can surpass the classical limit with entanglement assistance.
Research on Multi-Model Fusion Method for Similar Multi-domain

In order to improve the classification accuracy of test samples from different datasets in multi-domain image classification, this paper proposes a multi-domain sample classification method based on Baidu AI open platform image recognition and multi-model fusion. The classification task is divided into two steps: first, the domain to which the sample belongs is determined based on the predicted label and its probability output by the Baidu API, and then the deep learning model trained on that domain is used to predict the label of the sample. The effectiveness of the proposed method is verified through extensive experiments on three public datasets: Cifar-10, Cifar100, and Mini-ImageNet. In addition, compared with existing algorithms, the proposed method shows significant improvements in classification performance.
Semantic Segmentation of Urban Street Scenes Based on Prototype Learning and Neighborhood Attention

Semantic segmentation, as one of the fundamental topics in computer vision, aims to identify the class of each pixel in an image and has a wide range of applications in many fields. Traditional semantic segmentation models for dense prediction can be reduced to learning a single prototype of weight/query vectors for each class, which ignores the rich intra-class diversity and is fully parameterized in a way that does not take into consideration the representational power of the prototype and does not take fully exploit of the model’s segmentation capabilities. Hierarchical Transformers have gained a lot of interest in the vision domain due to their superior performance and ease of integration. These models usually employ local attention mechanisms, which effectively reduce the secondary complexity of the self-attention, but also lose the ability to capture long-range dependencies and the properties of the global receptive field. In this study, we propose DiPFormer, which introduces dilated neighborhood attention in the encoder part, which acts as an extension of neighborhood attention to capture more global dependencies and exponentially expands the receptive field without increasing the computational cost; and treats each class as a set of prototypes and directly shapes the pixel embedding space in the Decoder part, which is optimized by optimizing the distance for prediction. Evaluation results on the publicly available dataset Cityscapes show that the method achieves 83.89% mIoU, an improvement of 1.59 percentage points over the SegFormer, proving that the method is an effective improvement over baseline model.
Optical Character Recognition of Medical Records Based on Deep Learning

Retrieving the medical records of the insured is a crucial step in the insurance company’s claim investigation and helps to prevent insurance fraud. Due to the huge workload and sometimes even additional expenses incurred by entrusting a third party, it’s necessary to develop a medical record processor, which can automatically recognize medical records. Optical character recognition (OCR) has been a hot research topic in recent years owing to its outstanding ability to transmute scanned images and other visuals into text. A deep-learning-based OCR software was developed for the detection and recognition of medical records recognition and automatic calibration of medical records OCR in both Chinese and English are studied and realized, including classifying the physical drawings and scanned copies and removing the seal on the medical record sheet of scanned ones. Based on the observations from the analysis we give several suggestions related to the design and implementation of medical record processing approaches.
Primary brain tumors Image segmentation based on 3D-UNET with deep supervision and 3D brain modeling

Accurate 3D geometric modeling is essential for surgeons to diagnose and to operate in time. Clear and accurate image segmentation methods are the premise issue of accurate geometric modeling. Due to the complex characters of the brain tissue and the strong function of each part, in this paper, we proposed an image segmentation method based on the 3DU-net neural network with the deep supervision, which rapidly divided primary brain tumors from normal brain tissues. Three-dimensional geometric brain modeling are built so that surgeons may quickly make an accurate diagnose. A simulation platform was built based on Python software, visual studio software, Visualization Toolkit and 3D Slicer software. Experiments results show the accuracy and rapidity of the proposed algorithm.
Deep Learning-based Super-Resolution on the Cloud: Focus on Face and Text Enhancement

Real-ESRGAN and SwinIR are two deep learning models for Single-Image Super-Resolution (SISR), which attempt to address real-world scenarios for image enhancement. However, the pre-trained models do not effectively handle LR images containing human faces and text. An experiment is conducted to expand upon the training performed in their respective studies and improve the image enhancement using a cloud computing environment. Traditional image quality metrics, Peak Signal-toNoise Ratio (PSNR), and Structural Similarity (SSIM), are used to objectively evaluate the image quality. Three learning-based perceptual metrics, the Blind / Referenceless Image Spatial Quality Evaluator (BRISQUE), Naturalness Image Quality Evaluator (NIQE), and the Learned Perceptual Image Patch Similarity (LPIPS), are also incorporated to assess how images would be subjectively perceived based on human perception. To evaluate the model performance of Real-ESRGAN and SwinIR, specifically for face and text images, both traditional and perceptual metrics are taken into consideration, in addition to the cost associated with model training using Microsoft Azure. The findings show that with additional fine-tuning, SwinIR has slightly improved PSNR and SSIM values while taking less training time compared to Real-ESRGAN at the cost of perceptual quality.
Microwave-to-Optical Image Translation and Inversion Method Based on Improved Cycle GAN

In order to realize the image translation from microwave image to optical image and facilitate the visual understanding of microwave image, an optical image inversion method based on generative adversarial network is proposed. In this paper, through the designed MIMO antenna array and the improved bistatic RM algorithm based on spectrum separation, the curved square plate with patches is used for microwave imaging, and the microwave image and corresponding optical image are collected to construct a dataset. The generator network of Cycle GAN model is improved by introducing hourglass module, SE module and AdaLIN normalization method, which strengthens the feature extraction and feature fusion ability of the model for image internal semantics. Model comparison and ablation experiments show that the FID, KID and LPIPS score of the improved Cycle GAN model are 29.84, 0.1725 and 0.2011, respectively. And it has good optical image inversion ability. In this paper, the influence of dataset size and training epoch on the inversion performance of the improved Cycle GAN model is also studied. The results show that the improved Cycle GAN model trained on the large dataset with 200 epochs achieves a better optical image inversion effect.
PPC-US-LSF3DOD: A Pseudo-Point-Clouds based Unsupervised and Scalable Late-Sensor-Fusion Method for 3D Obstacle Detection<sup>*</sup>

To get the 3D coordinates and sizes of multi-scale obstacles, we propose an unsupervised and scalable late sensor fusion method using pseudo point clouds obtained from the stereo camera. In order to obtain high-quality depth maps and point clouds, we first perform various preprocessing and postprocessing on them, such as Speckle filter, probabilistic denoising, etc. Then we propose a multi-scale method for the problem of different ground heights and different scales of point cloud clustering. In addition, we also utilize an RGB monocular camera aligned with a stereo camera for late sensor fusion, which enables partially unlabeled point cloud obstacles to be assigned to a category. Our experimental results show that the proposed system achieves superior results on mobility scooters and electric wheelchairs without requiring a lot of computing resources and equipment costs.
Scalable Quantum Circuits for N-Qubit Unitary Matrices

This work presents an optimization-based scalable quantum neural network framework for approximating n-qubit unitaries through generic parametric representation of unitaries, which are obtained as product of exponential of elements of a new basis that we propose as an alternative to Pauli string basis. We call this basis as the Standard Recursive Block Basis, which is constructed using a recursive method, and its elements are permutation-similar to block Hermitian unitary matrices.
Handwritten Chinese Character Guidance System

With the general trend of informatization, the importance of handwritten Chinese characters is gradually being ignored. Due to the differences in the education levels and the writing habits of the public, problems such as confusion of stroke order, distorted fonts, and wrong postures for holding the pen always exist. Whether from the perspective of cultural inheritance or practicality, the learning of handwritten Chinese characters is an indispensable and important link in contemporary education. In this project, we hope that by introducing artificial intelligence and computer vision technology, we can monitor and guide students’ writing ability from the two aspects of pen grip posture and stroke order detection. The VGG16 Model was utilized to implement the function of pen grip posture classification. For the recognition of stroke order, it is hoped that a single Chinese character can be recognized first through real-time pen writing trajectories, so as to further perform the stroke extraction. CRNN (Convolutional Recurrent Neural Network) + CTPN (Connectionist Text Proposal Network) were applied for Handwriting Chinese characters recognition. SiamRPN++ model was implemented to track the pen tip region in the writing video, and through pen ballpoint detection and pen up/down classification, to achieve stroke segmentation of a single character. While in the last algorithm, the strokes would be classified as horizontal, vertical, oblique upwards, and oblique downwards according to the absolute value of the slope, and a length threshold and finally achieve the correctness judgment.
A self-supervised underwater image denoising method based on pseudo-siamese neural network

Underwater image denoising, as an important field of image enhancement in vision, has become an important and popular research field. Through image denoising, the texture features of the image will become clearer. In order to improve image quality, we present you a pseudo-siamese neural network based on self-supervised image denoising, which is specifically designed for underwater noise characteristics. Besides, the pyramid hierarchical are designed to consider the feature information under different scales at the same time in order to denoise the image noise. Totally, our model can reduce the noise caused by underwater impurities scattering significantly. We take not only the Structural Similarity (SSIM), Peak Signal-to-Noise Ratio (PSNR), Natural Image Quality Evaluator (NIQE) and Mean Opinion Score (MOS) but also the pose estimation (an important visual information during auto-underwater-vehicles docking)accuracy as evaluation criterion. Extensive experiments on self-built dataset and public datasets demonstrate the superior performance of our approach.
High quality GAN encoded using Transformer

StyleGAN inversion plays a crucial role in enabling the use of pre-trained StyleGAN for real image editing tasks. The goal of StyleGAN inversion is to find the exact latent encoding of a given real image within the latent space of StyleGAN. Existing optimization-based methods can produce high-quality results, but optimization often requires a significant amount of time. On the other hand, encoder-based methods are usually faster but suffer from lower result quality. In this paper, to improve the quality of results from encoder-based methods, we propose the following two enhancements: 1) employing a Transformer to encode image features, and 2) integrating the encoding of fine-grained details. By combining these design choices, we present an effective approach that not only achieves high-quality inversion results with encoder-based StyleGAN inversion but also effectively mitigates issues related to latent vector entanglement.
An Improved YOLOv7-tiny-based Lightweight Network for the Identification of Fish Species

In order to improve the speed of object detection, an improved YOLOv7-tiny-based lightweight network is proposed in this paper. The lightweight model, named as YOLOv7-tinyMobileOne model, is constructed by introducing a variant of MobileOne to replace the backbone network of YOLOv7-tiny. This model reduces the number of parameters and calculation expenses in the inference phase by pruning the network branch structure. Meanwhile, it alleviates the delay phenomenon of terminal devices. Then the proposed model achieves a marked improvement in detection efficiency, suitable for deployment on terminal devices. To verify the performance of the proposed model, we collected and annotated 7085 images of 17 fish species, and the following conclusions can be drawn from this dataset: (1) compared to YOLOv7, the parameter amount and the computational cost of YOLOv7-tiny-MobileOne decreased by 87.67% and by 89.94%, respectively, while the FPS of YOLOv7tiny-MobileOne increased by 284%; (2) compared to YOLOv7tiny, the parameter amount, and the computational cost of YOLOv7-tiny-MobileOne decreased by 25.45% and by 21.21%, respectively, while the FPS of YOLOv7-tiny-MobileOne increased by 9.94%. The experiments demonstrate that the improved model has a smaller volume and can achieve approximate accuracy while significantly improving identification speed, providing considerations for model deployment of terminal devices.