New Python Tools for Visual Debugging and Authoring of Tensor Networks and Quantum Circuits
Researchers have developed three complementary Python packages designed to help visualize and debug tensor networks and quantum circuits, which are typically easier to understand visually than as code. The tools—Tensor-Network-Visualization, Tensor-Network-Editor, and Quantum Circuit Drawer—function as a visual layer around existing libraries and quantum software development kits without implementing new algorithms or simulators. These packages address a practical problem in quantum computing and scientific computing workflows where structural mistakes in complex networks can be difficult to catch during development.
A new set of Python packages has been introduced to improve the development and debugging experience for tensor networks and quantum circuits. Tensor networks and quantum circuits are structural objects whose correctness depends on connectivity, indices, contraction order, gate placement, and other design choices—factors that are often clearer when visualized than when represented as code. The three packages work together to provide visual debugging, structural inspection, visual-to-code authoring with backend code generation, and circuit rendering with comparison capabilities. Importantly, these tools are not simulators and do not implement new contraction algorithms or execute quantum circuits themselves; rather, they form a visual inspection and authoring layer that integrates with existing tensor-network libraries, scientific Python workflows, and quantum SDKs. The contribution focuses on making structural artifacts visible, editable, inspectable, comparable, exportable, and reproducible within existing ecosystems.
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- arXiv physicsCenter
Visual-to-Code Authoring, Tensor-Network Debugging, and Quantum-Circuit Inspection Tools in Python
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