aiqbench analysis
Graphix introduces a dedicated Python framework designed specifically for measurement-based quantum computing (MBQC), addressing the current scarcity of software tools that connect theoretical models with practical research workflows. By offering a modular architecture for compiling and simulating MBQC protocols, it aims to streamline collaborative development and accelerate the transition toward practical implementations in this niche quantum paradigm.
Graphix: A software framework for Measurement-Based Quantum Computation
arXiv:2608.24781v1 Announce Type: new Abstract: Measurement-based quantum computing (MBQC) is a powerful model for quantum computation, but dedicated software tools that bridge its theoretical foundations with practical research workflows remain limited. We present Graphix, a software framework for MBQC written in Python that provides a unified environment for developing, integrating, and exploring measurement-based protocols and algorithms. Graphix establishes a modular, extensible, and user-friendly architecture for the compilation and simulation of quantum computations in the MBQC model with abstractions closely aligned with the theoretical formulations of MBQC. Through examples drawn from recent research on MBQC, we demonstrate Graphix's ability to reproduce and extend previous results. Graphix thus provides a software infrastructure for MBQC, supporting education and research while enabling collaborative development and accelerating the transition toward practical implementations.
Abstract:Measurement-based quantum computing (MBQC) is a powerful model for quantum computation, but dedicated software tools that bridge its theoretical foundations with practical research workflows remain limited. We present Graphix, a software framework for MBQC written in Python that provides a unified environment for developing, integrating, and exploring measurement-based protocols and algorithms. Graphix establishes a modular, extensible, and user-friendly architecture for the compilation and simulation of quantum computations in the MBQC model with abstractions closely aligned with the theoretical formulations of MBQC. Through examples drawn from recent research on MBQC, we demonstrate Graphix's ability to reproduce and extend previous results. Graphix thus provides a software infrastructure for MBQC, supporting education and research while enabling collaborative development and accelerating the transition toward practical implementations.
28 + 8 pages, 18 figures and 11 code snippets. Comments welcome!
arXiv:2608.24781 [quant-ph]
(or arXiv:2608.24781v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2608.24781 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 225 words
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