Studying few cluster resonances with quantum neural network driven iterative Harrow-Hassidim-Lloyd algorithm

• URL: http://arxiv.org/abs/2507.00074v1
• Date: Sun, 29 Jun 2025 05:28:14 GMT
• Title: Studying few cluster resonances with quantum neural network driven iterative Harrow-Hassidim-Lloyd algorithm
• Authors: Hantao Zhang, Dong Bai, Zhongzhou Ren,
• Abstract summary: We use quantum computing to investigate the properties of hypernuclei $5_Lambda$He, $ 6_Lambda$He and $9_Lambda$Be.<n>Our approach combines quantum neural network (QNN) with iterative Harrow-Hassidim-Lloyd (IHHL) algorithm to solve the quantum many-body problem.
• Score: 0.40964539027092917
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: By using the quantum computing the properties of hypernuclei ${}^5_{\Lambda}$He, ${}^{\ 6}_{{\Lambda\Lambda}}$He and ${}^9_{\Lambda}$Be can be investigated within microscopic cluster model. Our approach combines quantum neural network (QNN) with iterative Harrow-Hassidim-Lloyd (IHHL) algorithm (abbreviated as QNN-IHHL) to solve the quantum many-body problem. To efficiently describe resonance phenomena, we employ complex scaling and eigenvector continuation techniques, providing a robust framework for identifying few-cluster resonance parameters within quantum computing. To validate our quantum algorithm, the resonant $4^{+}$ state of ${}^9_{\Lambda}$Be is chosen as a core example. With QNN-IHHL algorithm we realize a fully quantum workflow, which provides a novel framework and some ground work for exploring resonance properties in complex nuclear many-body systems.

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