Quantum Tensor Representation via Circuit Partitioning and Reintegration

• URL: http://arxiv.org/abs/2511.05492v1
• Date: Fri, 07 Nov 2025 18:59:58 GMT
• Title: Quantum Tensor Representation via Circuit Partitioning and Reintegration
• Authors: Ziqing Guo, Jan Balewski, Kewen Xiao, Ziwen Pan,
• Abstract summary: We introduce shardQ, a method that leverages the SparseCut algorithm with matrix product state compilation and a global knitting technique.<n>This study also presents the results regarding application readiness.
• Score: 0.6766416093990318
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum computing enables faster computations than clas-sical algorithms through superposition and entanglement. Circuit cutting and knitting are effective techniques for ame-liorating current noisy quantum processing unit (QPUs) er-rors via a divide-and-conquer approach that splits quantum circuits into subcircuits and recombines them using classical post-processing. The development of circuit partitioning and recomposing has focused on tailoring the simulation frame-work by replacing generic non-local gates with probabilistic local gates and measuring the classical communication com-plexity. Designing a protocol that supports algorithms and non-all-to-all qubit-connected physical hardware remains underdeveloped owing to the convoluted properties of cut-ting compact controlled unitary gates and hardware topology. In this study, we introduce shardQ, a method that leverages the SparseCut algorithm with matrix product state (MPS) compilation and a global knitting technique. This method elucidates the optimal trade-off between the computational time and error rate for quantum encoding with a theoretical proof, evidenced by an ablation analysis using an IBM Mar-rakesh superconducting-type QPU. This study also presents the results regarding application readiness.

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