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Quantum computing is one of the most promising technology advances of the latest years. Qubits are highly sensitive to noise, which can make the output useless. Lately, it has been shown that superconducting qubits are extremely susceptible to external sources of faults, such as ionizing radiation. When adopted in large scale, radiation-induced errors are expected to become a serious challenge for qubits reliability. We propose an evaluation of the impact of transient faults in the execution of quantum circuits on superconducting chips. Inspired by the Architectural and Program Vulnerability Factors, widely used for classical computation, we propose the Quantum Vulnerability Factor (QVF) to measure the impact of qubit corruption on the circuit output. We model faults, and design a fault injector, based on the latest studies on real machines and radiation experiments. We report the finding of more than 388,000,000 fault injections, considering single and double faults, on three algorithms, identifying the faults and qubits that are more likely to impact the output. We give guidelines on how to map the qubits in real devices to reduce the output error and to reduce the probability of having a radiation-induced corruption modifying the output. Finally, we compare simulations with experiments on physical quantum computers.

In this letter, we propose a novel Non-Line-of-Sight (NLOS) identification and error-mitigation method for dynamic object positioning and ultra-wideband (UWB) ranging. By utilizing inverse estimation on known Anchor Points (APs) and improved robust unscented Kalman filter (IRUKF), while fusing Gyroscope and Accelerometer data, the proposed technology identifies and compensates for NLOS occlusions between tag and APs, reducing positioning errors. The approach has been verified through simulation and experiment, with identification precision of 97.02%. After mitigating errors, substantial error reductions of 91.80% and 98.90% were observed in LOS and NLOS situations, respectively. Moreover, the developed IRUKF effectively minimizes mislocalization by 50.48% in harsh scenarios.

A wide range of collaborative human-robot applications across diverse domains, encompassing manufacturing, industry, and social interactions, necessitate an autonomous robot to follow its human companion. However, the great degree of flexibility and unpredictability of human mobility makes the integration of multi-sensor measurements for human-following difficult. This paper addresses these challenges by demonstrating the design and implementation of a multi-sensor person-following system. On the one hand, a target tracking control method based on detection of center boxes is proposed. On the other hand, the positioning, navigation and tracking modules are integrated into the robot system. Exhaustive functional verification is conducted on an Ackermann-steering mobile robot, in which an embedded device is the core algorithm board. Notably, we discuss the degradation phenomenon of Light Detection and Ranging (LiDAR) odometry. The positioning error of our system is 2.42%, which is much smaller than other advanced methods. In indoor experiments, our system enables the robot to stably track the target at a distance of approximately 1.5 m. The effectiveness of the entire system is verified, demonstrating its advantage in real-time performance.

With the rapid development of Quantum Machine Learning, quantum neural networks (QNN) have experienced great advancement in the past few years, harnessing the advantages of quantum computing to significantly speed up classical machine learning tasks. Despite their increasing popularity, the quantum neural network is quite counter-intuitive and difficult to understand, due to their unique quantum-specific layers (e.g., data encoding and measurement) in their architecture. It prevents QNN users and researchers from effectively understanding its inner workings and exploring the model training status. To fill the research gap, we propose VIOLET, a novel visual analytics approach to improve the explainability of quantum neural networks. Guided by the design requirements distilled from the interviews with domain experts and the literature survey, we developed three visualization views: the Encoder View unveils the process of converting classical input data into quantum states, the Ansatz View reveals the temporal evolution of quantum states in the training process, and the Feature View displays the features a QNN has learned after the training process. Two novel visual designs, i.e., satellite chart and augmented heatmap, are proposed to visually explain the variational parameters and quantum circuit measurements respectively. We evaluate VIOLET through two case studies and in-depth interviews with 12 domain experts. The results demonstrate the effectiveness and usability of VIOLET in helping QNN users and developers intuitively understand and explore quantum neural networks.