Quantum computing users increasingly access quantum hardware through cloud platforms and are charged based on usage. Because these resources are scarce and expensive, users often request specific devices to ensure performance, while providers may reassign jobs to other devices to maximize throughput, potentially compromising user expectations. Thus, we present QuanGuard, an efficient fingerprinting framework for verifying whether the allocated quantum resources match user selections. QuanGuard constructs dynamic fingerprints from the noisy execution results of a probing circuit, exploiting device-specific error evolution patterns to identify hardware uniquely. We further develop an error evolution algorithm that generates user-side fingerprints for lightweight matching against assigned resources. The method requires only a single probing circuit per detection, making it highly practical for current quantum cloud platforms. Experiments on seven IBM quantum computers show that QuanGuard achieves accurate and reliable device verification with minimal overhead.

Noisy Intermediate-Scale Quantum (NISQ) devices, while accessible via cloud platforms, face challenges due to limited availability and suboptimal quality. These challenges raise the risk of cloud providers offering fraudulent services. This emphasizes the need for users to detect such fraud to protect their investments and ensure computational integrity. This study introduces a novel dynamic fingerprinting method for detecting fraudulent service provision on quantum cloud platforms, specifically targeting machine substitution and profile fabrication attacks. The dynamic fingerprint is constructed using a single probing circuit to capture the unique error characteristics of quantum devices, making this approach practical because of its trivial computational costs. When the user examines the service, the execution results of the probing circuit act as the device-side fingerprint of the quantum device providing the service. The user then generates the user-side fingerprint by estimating the expected execution result, assuming the correct device is in use. We propose an algorithm for users to construct the user-side fingerprint with linear complexity. By comparing the device-side and user-side fingerprints, users can effectively detect fraudulent services. Our experiments on the IBM Quantum platform, involving seven devices with varying capabilities, confirm the method's effectiveness.

Quantum networks encounter unavoidable channel noises and erasure errors, presenting a huge obstacle in designing protocols that attain both high reliability and efficiency. Typically, quantum networks fall into two categories: those utilize quantum entanglements for quantum teleportation, and those directly transfer the actual quantum messages. In this paper, we present SurfNet, a quantum network that inherits the main advantages from both categories. It employs surface codes as logical qubits for encoding messages, and utilizes two parallel communication channels to fault-tolerantly transfer each surface code in a modular manner. Our approach of using surface codes can timely correct both operational and photon loss errors within the network, and the integration of the two channels within the network can greatly improve network throughput. For the implementation of SurfNet, we propose a novel network architecture, designed to better integrate surface codes into quantum networks. We also propose a novel error correction decoder, designed to fully utilize the modular characteristic of surface codes within our network. Simulation results demonstrate that SurfNet with its decoder significantly enhances the communication fidelity within quantum networks.

Recently, Transformer-based methods have achieved significant improvements over convolutional neural networks (CNNs) in single image deraining, due to the powerful ability of modeling non-local information. In fact, rich local-global information representations are equally important for better satisfying rain removal. In this paper, we propose an effective image deraining method by integrating a CNN model into the Transformer backbone to accelerate network convergence, called Multi-scale Dilated-convolution Transformer (MDT), which fully leverages the learning capabilities of Transformers on non-local features, seamlessly integrating local detail extraction and global structural representation. The fundamental building unit of our framework is the Multi-scale Dilated-convolution Transformer Block (MDTB) with different dilation rates, which consists of the Dilconv Self-Attention (DSA) and the Dilconv Feed-Forward Network (DFN). Specifically, the former processes the contextual information via dilated convolutions and enables the model to emphasize spatially-varying rain distribution features, while the latter integrates the dual-branch information to facilitate the local feature learning for better feature aggregation. Extensive evaluations demonstrate that our model reaches superior performance, significantly improving the image deraining quality.

The convolutional neural network (CNN) has become a general approach for image processing in machine learning tasks. Quantum CNN (QCNN) is an emerging method to implement CNN using quantum computing. Quantum computing utilizes the properties of quantum mechanics to perform efficient computing. However, current quantum machines do not support large-scale QCNNs due to a lack of qubits. As a consequence, QCNNs are limited in scale and cannot directly process high-dimensional images. These shortcomings result in suboptimal QCNN performance. Meanwhile, building quantum machines with enough qubits is technically difficult and costly. These obstacles motivate us to design a quantum edge computing (QEC) system capable of achieving the scalability of QCNNs. Quantum machines are organized hierarchically in the QEC system. The quantum machines closer to the users collaboratively load and extract quantum features from the high-dimensional input data. Subsequently, the quantum machine in the next layer collects the extracted features and performs further operations to produce the final results. Each quantum machine in the QEC system is equipped with a local small-scale QCNN to capture the data pattern of its input. The local QCNNs could be combined to form a large-scale QCNN capable of learning and processing high-dimensional data, overcoming hardware limitations and improving performance.

A large volume of data is generated by ubiquitous Internet-of-Things (IoT) devices and utilized to train machine learning models by IoT manufacturers to provide users with better services. Many deep learning systems for IoT data are required to perform all computation locally on small devices, which is not suitable for these resource-constrained devices. The devices can also send all the collected data to a server for costly model training by ignoring privacy concerns. To design an efficient and secure deep learning model training system, in this paper, we propose a federated learning system on the edge using the differential privacy mechanism to protect sensitive information and offload computation work from edge devices to edge servers, with consideration of communication reduction. In our system, a large-scale deep learning model is partitioned onto edge devices and edge servers, and trained in a distributed manner, in which all untrusted components are prevented from retrieving protected information from the training and inference process. We evaluate the proposed approach with respect to computation, communication, and privacy protection. The experiment results show that the proposed approach can preserve users’ privacy while significantly reducing computation and communication costs.

Federated Learning is a machine learning scheme in which a shared prediction model can be collaboratively learned by a number of distributed nodes using their locally stored data. It can provide better data privacy because training data are not transmitted to a central server. Federated learning is well suited for edge computing applications and can leverage the the computation power of edge servers and the data collected on widely dispersed edge devices. To build such an edge federated learning system, we need to tackle a number of technical challenges. In this survey, we provide a new perspective on the applications, development tools, communication efficiency, security & privacy, migration and scheduling in edge federated learning.