Quantum computing is a rapidly evolving field that enables exponential speed-up over classical algorithms. At the heart of this revolutionary technology are quantum circuits, which serve as vital tools for implementing, analyzing, and optimizing quantum algorithms. Recent advancements in quantum computing and the increasing capability of quantum devices have led to the development of more complex quantum circuits. However, traditional quantum circuit diagrams suffer from scalability and readability issues, which limit the efficiency of analysis and optimization processes. In this research, we propose a novel visualization approach for large-scale quantum circuits by adopting semantic analysis to facilitate the comprehension of quantum circuits. We first exploit meta-data and semantic information extracted from the underlying code of quantum circuits to create component segmentations and pattern abstractions, allowing for easier wrangling of massive circuit diagrams. We then develop Quantivine, an interactive system for exploring and understanding quantum circuits. A series of novel circuit visualizations is designed to uncover contextual details such as qubit provenance, parallelism, and entanglement. The effectiveness of Quantivine is demonstrated through two usage scenarios of quantum circuits with up to 100 qubits and a formal user evaluation with quantum experts.

In history research, cohort analysis seeks to identify social structures and figure mobilities by studying the group-based behavior of historical figures. Prior works mainly employ automatic data mining approaches, lacking effective visual explanation. In this paper, we present CohortVA, an interactive visual analytic approach that enables historians to incorporate expertise and insight into the iterative exploration process. The kernel of CohortVA is a novel identification model that generates candidate cohorts and constructs cohort features by means of pre-built knowledge graphs constructed from large-scale history databases. We propose a set of coordinated views to illustrate identified cohorts and features coupled with historical events and figure profiles. Two case studies and interviews with historians demonstrate that CohortVA can greatly enhance the capabilities of cohort identifications, figure authentications, and hypothesis generation.

The development of digitized humanity information provides a new perspective on data-oriented studies of history. Many previous studies have ignored uncertainty in the exploration of historical figures and events, which has limited the capability of researchers to capture complex processes associated with historical phenomena. We propose a visual reasoning system to support visual reasoning of uncertainty associated with spatio-temporal events of historical figures based on data from the China Biographical Database Project. We build a knowledge graph of entities extracted from a historical database to capture uncertainty generated by missing data and error. The proposed system uses an overview of chronology, a map view, and an interpersonal relation matrix to describe and analyse heterogeneous information of events. The system also includes uncertainty visualization to identify uncertain events with missing or imprecise spatio-temporal information. Results from case studies and expert evaluations suggest that the visual reasoning system is able to quantify and reduce uncertainty generated by the data.

Quantum neural networks (QNNs) hold immense potential for complex tasks by harnessing quantum entanglement and superposition, such as physics simulation, artificial intelligence, and cryptography. However, the presence of quantum noise, stemming from hardware imperfections and environmental interactions, significantly reduces their practical performance. Moreover, the noise varies from different devices and shifts over time, necessitating continuous retraining models to chase and cater to the evolving noise, leading to high-computation costs. In this article, we present QuST, a novel QNN robust training framework designed to handle the noise in a once-and-for-all manner, which can tackle both spatial and temporal biases to maintain the QNN model accuracy under ever-changing noise conditions. Our approach consists of three key components. First, we propose a metric called circuit sequence correctness (CSC) to characterize QNN circuit reliability in noisy environments. Then, we model CSC as a training weight to incorporate loss integration and utilize KL divergence to align noise inference with noise-free inference, thereby improving anti-noise capabilities. Furthermore, we introduce multiscale noise-aware training to enhance the model’s noise tolerance at various noise magnitudes. We conduct experiments on MNIST and fashion-MNIST datasets, along with 190-day historical noise simulations and one case study on 7 real IBMQ quantum computers. The results demonstrate 8.1%–15.1% and 9.1%–11.45% accuracy improvements in temporal and spatial dimensions, respectively. Additionally, we conduct ablation experiments to validate the effectiveness of the QuST’s key components. The results demonstrate that QuST consistently sustains high accuracy without retraining,even under changing noise conditions, and exhibits minimal loss of accuracy as noise levels increase.

As the COVID-19 pandemic continues to threaten various regions around the world, obtaining accurate and reliable COVID-19 data is crucial for governments and local communities aiming at rigorously assessing the extent and magnitude of the virus spread and deploying efficient interventions. Using data reported between January and February 2020 in China, we compared counts of COVID-19 from near-real-time spatially disaggregated data (city level) with fine-spatial scale predictions from a Bayesian downscaling regression model applied to a reference province-level data set. The results highlight discrepancies in the counts of coronavirus-infected cases at the district level and identify districts that may require further investigation.

Quantum readout noise turns out to be the most significant source of error, which greatly affects the measurement fidelity. Matrix-based calibration has been demonstrated to be effective in various quantum platforms. However, existing methodologies are fundamentally limited in either scalability or accuracy. Inspired by the classical finite element method (FEM), a formal method to model the complex interaction between elements, we present our calibration framework named QuFEM. First, we apply a divide-and-conquer strategy that formulates the calibration as a series of tensor products with noise matrices. This matrices are iteratively characterized together with the calibrated probability distribution, aiming to capture the inherent locality of qubit interactions. Then, to accelerate the end-to-end calibration, we propose a sparse tensor-product engine to exploit the sparsity in the intermediate values. Our experiments show that QuFEM achieves 2.5×10³× speedup in the 136-qubit calibration compared to the state-of-the-art matrix-based calibration technique [50], and provides 1.2× and 1.4× fidelity improvement on the 18-qubit and 36-qubit real-world quantum devices.