References & Citations
Quantum Physics
Title: Estimating Quantum Mutual Information Through a Quantum Neural Network
(Submitted on 26 Jun 2023 (v1), last revised 11 Feb 2024 (this version, v2))
Abstract: We propose a method of quantum machine learning called quantum mutual information neural estimation (QMINE) for estimating von Neumann entropy and quantum mutual information, which are fundamental properties in quantum information theory. The QMINE proposed here basically utilizes a technique of quantum neural networks (QNNs), to minimize a loss function that determines the von Neumann entropy, and thus quantum mutual information, which is believed more powerful to process quantum datasets than conventional neural networks due to quantum superposition and entanglement. To create a precise loss function, we propose a quantum Donsker-Varadhan representation (QDVR), which is a quantum analog of the classical Donsker-Varadhan representation. By exploiting a parameter shift rule on parameterized quantum circuits, we can efficiently implement and optimize the QNN and estimate the quantum entropies using the QMINE technique. Furthermore, numerical observations support our predictions of QDVR and demonstrate the good performance of QMINE.
Submission history
From: Myeongjin Shin [view email][v1] Mon, 26 Jun 2023 10:26:45 GMT (685kb,D)
[v2] Sun, 11 Feb 2024 07:12:21 GMT (304kb,D)
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