An Adaptive Weighted QITE-VQE Algorithm for Combinatorial Optimization Problems

• URL: http://arxiv.org/abs/2504.10651v1
• Date: Mon, 14 Apr 2025 19:05:16 GMT
• Title: An Adaptive Weighted QITE-VQE Algorithm for Combinatorial Optimization Problems
• Authors: Ningyi Xie, Xinwei Lee, Tiejin Chen, Yoshiyuki Saito, Nobuyoshi Asai, Dongsheng Cai,
• Abstract summary: We propose an Adaptive Weighted QITE-VQE (AWQV) algorithm that integrates the VQE gradients with the cQITE updates through an adaptive weighting scheme during optimization.<n>AWQV achieves near-optimal approximation ratios, while for weighted ErdHos-R'enyi instances, it outperforms the classical Goemans-Williamson algorithm.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The variational quantum eigensolver (VQE) is a promising algorithm for finding the ground states of a given Hamiltonian. Its application to binary-formulated combinatorial optimization (CO) has been intensively studied in recent years. However, typical VQE approaches for CO problems often suffer from local minima or barren plateaus, limiting their ability to achieve optimal solutions. The quantum imaginary time evolution (QITE) offers an alternative approach for effective ground-state preparation but requires large circuits to approximate non-unitary operations. Although compressed QITE (cQITE) reduces circuit depth, accumulated errors eventually cause energy increases. To address these challenges, we propose an Adaptive Weighted QITE-VQE (AWQV) algorithm that integrates the VQE gradients with the cQITE updates through an adaptive weighting scheme during optimization. In numerical experiments of MaxCut on unweighted regular graphs, AWQV achieves near-optimal approximation ratios, while for weighted Erd\H{o}s-R\'enyi instances, it outperforms the classical Goemans-Williamson algorithm.

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