QuantumEyes: Towards Better Interpretability of Quantum Circuits
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Abstract (arXiv)
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 QuantumEyes 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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Nov 2023.
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Citing papers per year: 2024: 2; 2025: 3; 2026: 1.
6 papers.
- 2024Quantum Adjoint Convolutional Layers for Effective Data Representationcited by 2
- 2025Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Surveycited by 2
- 2024A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelitycited by 1
- 2025Detecting underdetermination in parameterized quantum circuitscited by 0
- 2025Towards Explainable Quantum AI: Informing the Encoder Selection of Quantum Neural Networks via Visualizationcited by 0
- 2026Quantum Cinema: An Interactive Cinematic Exploration of Quantum Computing Hardware via Generative World Modelscited by 0
- 2026Quantum Cinema: An Interactive Cinematic Exploration of Quantum Computing Hardware via Generative World Models14 Jun 2026
- 2025Towards Explainable Quantum AI: Informing the Encoder Selection of Quantum Neural Networks via Visualization16 Dec 2025
- 2025Towards Adapting Federated & Quantum Machine Learning for Network Intrusion Detection: A Survey24 Sep 2025
- 2025Detecting underdetermination in parameterized quantum circuits4 Apr 2025
- 2024Quantum Adjoint Convolutional Layers for Effective Data Representation26 Apr 2024
- 2024A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity23 Feb 2024