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. A free copy of this paper and all supplemental materials are available at https://osf.io/2m9yh/?view_only=0aa1618c97244f5093cd7ce15f1431f9.
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. A free copy of this paper and all supplemental materials are available at https://osf.io/2m9yh/?view_only=0aa1618c97244f5093cd7ce15f1431f9.
Driven by potential exponential speedups in business, security, and scientific scenarios, interest in quantum computing is surging. This interest feeds the development of quantum computing hardware, but several challenges arise in optimizing application performance for hardware metrics (e.g., qubit coherence and gate fidelity). In this work, we describe a visual analytics approach for analyzing the performance properties of quantum devices and quantum circuit optimization. Our approach allows users to explore spatial and temporal patterns in quantum device performance data and it computes similarities and variances in key performance metrics. Detailed analysis of the error properties characterizing individual qubits is also supported. We also describe a method for visualizing the optimization of quantum circuits. The resulting visualization tool allows researchers to design more efficient quantum algorithms and applications by increasing the interpretability of quantum computations.
Quantum computing offers significant speedup compared to classical computing, which has led to a growing interest among users in learning and applying quantum computing across various applications. However, quantum circuits, which are fundamental for implementing quantum algorithms, can be challenging for users to understand due to their underlying logic, such as the temporal evolution of quantum states and the effect of quantum amplitudes on the probability of basis quantum states. To fill this research gap, we propose QuantumEyes, an interactive visual analytics system to enhance the interpretability of quantum circuits through both global and local levels. For the global-level analysis, we present three coupled visualizations to delineate the changes of quantum states and the underlying reasons: a Probability Summary View to overview the probability evolution of quantum states; a State Evolution View to enable an in-depth analysis of the influence of quantum gates on the quantum states; a Gate Explanation View to show the individual qubit states and facilitate a better understanding of the effect of quantum gates. For the local-level analysis, we design a novel geometrical visualization dandelion chart to explicitly reveal how the quantum amplitudes affect the probability of the quantum state. We thoroughly evaluated QuantumEyes as well as the novel dandelion chart integrated into it through two case studies on different types of quantum algorithms and in-depth expert interviews with 12 domain experts. The results demonstrate the effectiveness and usability of our approach in enhancing the interpretability of quantum circuits.
Recently, various researches and developments have been made to create a new computer that exceeds the capacity of a classical computer by utilizing a quantum computer. Quantum computing controls with operations of quantum bits called Qubit and uses various quantum physics phenomena to perform computations faster than classical computing. The quantum physics phenomena such as quantum entanglement and quantum superposition are used to carry out the computation. Although conventional classical computing systems can easily understand the current state of the system with pre-defined logic gates, symbols and procedures, on the other hand, the quantum computer have a complicated structure and difficult to intuitively understand. For example, when the number of controllable quantum bits hugely increases, complex quantum operations by quantum computing algorithms is generated. Also, to visualize large-scale complex quantum computer circuits, we need better ways to visualize quantum circuits. In this paper, in the process of designing a large-scale quantum computer system, the complex quantum algorithm and quantum system generated by controlling a large number of quantum bits are hierarchically visualized so that users can more effectively recognize the quantum computer system. In order to effectively design a computing circuit, we propose a quantum computing circuit visualization system using a hologram-like system.
Quantum computing has the potential to tackle complex problems that are intractable for classical computers, thanks to its unique properties, such as superposition and entanglement. Nevertheless, developing efficient quantum programs remains challenging because of the diversity and rapid evolution of hardware, and limited access to quantum devices. To tackle these issues, we propose an open-source, profile-guided development flow that allows programmers to refine and optimize quantum code using quantum circuit simulators to evaluate program performance without the need for physical quantum hardware. In the proposed flow, the performance data collected in previous simulation runs helps guide compiler optimizations to generate efficient quantum circuits. An integrated visualization tool further aids programmers by associating performance data with the quantum program code, making it easier to pinpoint code regions for potential improvement. This development flow offers a practical path toward the development of novel and efficient quantum algorithms for various quantum computing platforms.
With the emergence of more and more applications for quantum computing, also the development of corresponding methods for design automation is receiving increasing interest. In this respect, decision diagrams provide a promising basis for many design tasks such as simulation, synthesis, verification, and more. However, users of the corresponding tools often do not have a proper background or an intuition about how these methods based on decision diagrams work and what their strengths and limits are. In an effort to make decision diagrams for quantum computing more accessible, we present a visualization tool which visualizes quantum decision diagrams and allows to explore their behavior when used in the design tasks mentioned above. The installation-free web-tool allows users to interactively learn how decision diagrams can be used in quantum computing, e.g., to (1) compactly represent quantum states and the functionality of quantum circuits, (2) to efficiently simulate quantum circuits, and (3) to verify the equivalence of two circuits. The tool is available at https://iic.jku.at/eda/research/quantum_dd/tool
Quantum computing has entered a stage of increasing practicality. Many quantum hardware vendors such as IBM, Rigetti, Honeywell, and IonQ now enable experiments on real devices in the Noisy Intermediate-Scale Quantum (NISQ) era. These platforms show computational advantages in domains such as optimization, machine learning, and materials science. However, they remain limited by hardware noise and the absence of human-interpretable information. Existing visual metaphors, such as the Bloch Sphere for single-qubit states or circuit schematics for algorithm design, struggle to convey multi-qubit entanglement or measurement probabilities in ways accessible to human reasoning. Likewise, the rise of variational quantum circuits and quantum neural networks (QNNs) introduces new layers of opacity: how data are encoded into quantum states and how parameters evolve through training remain largely under-explored. These challenges motivate a need for visual explanation frameworks that unveil the invisible processes of quantum computing visible, interpretable, and reasonable.To address these challenges, this dissertation presents a coherent sequence of visual analytics systems that progressively enhance explainability across the hardware, state, circuit, and model layers of quantum computing. First, VACSEN supports noise-aware execution by visually correlating hardware fidelity metrics with compiled quantum circuits, allowing users to identify unstable qubits, track temporal noise evolution, and select optimal execution strategies. Second, VENUS introduces a geometric representation for quantum state visualization, combining right triangles and semicircles to jointly encode amplitude and probability information, thereby enabling intuitive exploration of superposition and entanglement beyond the traditional Bloch Sphere. Third, QuantumEyes extends interpretability to quantum circuit behavior, integrating coordinated views to reveal how quantum gates transform amplitudes and probabilities during algorithm execution (e.g., Grover’s search or the Quantum Fourier Transform). Finally, VIOLET applies visual explainability to quantum neural networks, bridging concepts from explainable AI and quantum computing. It visualizes the encoding of classical data into quantum states, tracks parameter dynamics during variational training, and exposes learned features and measurement distributions, establishing the first end-to-end system for understanding QNNs.Each system was designed through iterative co-design with quantum computing experts and evaluated through case studies, task analyses, and in-depth interviews involving researchers and practitioners from academia and industry. Across evaluations, participants reported that visualization significantly enhanced their ability to reason about noise effects, understand quantum-state evolution, and interpret the learning behavior of QNNs. These findings demonstrate that visualization not only accelerates analysis and debugging but also strengthens conceptual understanding and user trust in quantum computing. To sum up, this dissertation establishes visualization