Quantivine: A Visualization Approach for Large-scale Quantum Circuit Representation and Analysis
Zhen Wen, Yihan Liu, Siwei Tan, Jieyi Chen, Minfeng Zhu, Dongming Han,, Jianwei Yin, Mingliang Xu, and Wei Chen

TL;DR
Quantivine introduces a novel visualization system that leverages semantic analysis to improve the scalability and interpretability of large-scale quantum circuits, aiding quantum computing research and development.
Contribution
The paper presents a new visualization approach and interactive system, Quantivine, for analyzing complex quantum circuits using semantic information to enhance understanding and scalability.
Findings
Successfully visualized circuits with up to 100 qubits.
Demonstrated improved comprehension through user evaluation.
Enabled analysis of qubit provenance, parallelism, and entanglement.
Abstract
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…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Neural Networks and Reservoir Computing · Online Learning and Analytics
