Disentangling quantum neural networks for unified estimation of quantum entropies and distance measures

• URL: http://arxiv.org/abs/2401.07716v3
• Date: Thu, 02 Jan 2025 05:39:38 GMT
• Title: Disentangling quantum neural networks for unified estimation of quantum entropies and distance measures
• Authors: Myeongjin Shin, Seungwoo Lee, Junseo Lee, Mingyu Lee, Donghwa Ji, Hyeonjun Yeo, Kabgyun Jeong,
• Abstract summary: We introduce the disentangling quantum neural network (DEQNN), designed to efficiently estimate various physical quantities in quantum information. Our proposed DEQNN offers a unified dimensionality reduction methodology that can significantly reduce the size of the Hilbert space while preserving the values of diverse physical quantities.
• Score: 2.14566083603001
• License:
• Abstract: The estimation of quantum entropies and distance measures, such as von Neumann entropy, R\'{e}nyi entropy, Tsallis entropy, trace distance, and fidelity-induced distances such as the Bures distance, has been a key area of research in quantum information science. In our study, we introduce the disentangling quantum neural network (DEQNN), designed to efficiently estimate various physical quantities in quantum information. Estimation algorithms for these quantities are generally tied to the size of the Hilbert space of the quantum state to be estimated. Our proposed DEQNN offers a unified dimensionality reduction methodology that can significantly reduce the size of the Hilbert space while preserving the values of diverse physical quantities. We provide an in-depth discussion of the physical scenarios and limitations in which our algorithm is applicable, as well as the learnability of the proposed quantum neural network.

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