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This paper introduces a suite of Python packages that provide a visual authoring and inspection layer for tensor networks and quantum circuits, addressing the difficulty of debugging structural artifacts in code-only workflows. These tools focus on visualization, code generation, and export capabilities rather than simulation, aiming to make complex quantum designs more visible and reproducible within existing scientific Python ecosystems. The release offers a practical utility for developers working with quantum SDKs who need better structural clarity without introducing new contraction algorithms or execution backends.
Visual-to-Code Authoring, Tensor-Network Debugging, and Quantum-Circuit Inspection Tools in Python
arXiv:2606.08760v1 Announce Type: new Abstract: Tensor networks and quantum circuits are structural objects whose meaning depends on connectivity, indices, contraction order, gate placement, measurements, and related design choices. They are often easier to reason about visually than as code, yet in Python they are frequently constructed, transformed, and checked through backend-specific objects or compact symbolic expressions. This can make structural mistakes hard to notice during development, debugging, and communication. This paper presents three complementary packages: Tensor-Network-Visualization for visual debugging and structural inspection of supported tensor-network and traced einsum workflows; Tensor-Network-Editor for visual-to-code authoring, backend code generation, JSON preservation, export, and design-level analysis; and Quantum Circuit Drawer for clear circuit rendering, inspection, and complementary comparison of circuits or documented result distributions. The packages form a visual authoring and inspection layer around existing tensor-network libraries, array-based scientific Python workflows, and quantum SDKs. They are not simulators: they do not implement new contraction algorithms, execute quantum circuits, or guarantee full semantic equivalence across arbitrary backends. Their contribution is to make structural artifacts visible, editable, inspectable, comparable, exportable, and reproducible within those ecosystems.
Abstract:Tensor networks and quantum circuits are structural objects whose meaning depends on connectivity, indices, contraction order, gate placement, measurements, and related design choices. They are often easier to reason about visually than as code, yet in Python they are frequently constructed, transformed, and checked through backend-specific objects or compact symbolic expressions. This can make structural mistakes hard to notice during development, debugging, and communication. This paper presents three complementary packages: Tensor-Network-Visualization for visual debugging and structural inspection of supported tensor-network and traced einsum workflows; Tensor-Network-Editor for visual-to-code authoring, backend code generation, JSON preservation, export, and design-level analysis; and Quantum Circuit Drawer for clear circuit rendering, inspection, and complementary comparison of circuits or documented result distributions. The packages form a visual authoring and inspection layer around existing tensor-network libraries, array-based scientific Python workflows, and quantum SDKs. They are not simulators: they do not implement new contraction algorithms, execute quantum circuits, or guarantee full semantic equivalence across arbitrary backends. Their contribution is to make structural artifacts visible, editable, inspectable, comparable, exportable, and reproducible within those ecosystems.
Quantum Physics (quant-ph); Computational Physics (physics.comp-ph)
arXiv:2606.08760 [quant-ph]
(or arXiv:2606.08760v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2606.08760 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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