Layerwise Quantum Convolutional Neural Networks Provide a Unified Way
for Estimating Fundamental Properties of Quantum Information Theory
- URL: http://arxiv.org/abs/2401.07716v1
- Date: Mon, 15 Jan 2024 14:33:03 GMT
- Title: Layerwise Quantum Convolutional Neural Networks Provide a Unified Way
for Estimating Fundamental Properties of Quantum Information Theory
- Authors: Myeongjin Shin, Seungwoo Lee, Mingyu Lee, Donghwa Ji, Hyeonjun Yeo,
Harrison J. Lee, Kabgyun Jeong
- Abstract summary: This paper proposes a unified methodology using Layerwise Quantum Convolutional Neural Networks (LQCNN)
Recent studies exploring parameterized quantum circuits for property estimation face challenges such as barren plateaus and complexity issues in large qubit states.
- Score: 1.609244566234922
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The estimation of fundamental properties in quantum information theory,
including von Neumann entropy, R\'enyi entropy, Tsallis entropy, quantum
relative entropy, trace distance, and fidelity, has received significant
attention. While various algorithms exist for individual property estimation, a
unified approach is lacking. This paper proposes a unified methodology using
Layerwise Quantum Convolutional Neural Networks (LQCNN). Recent studies
exploring parameterized quantum circuits for property estimation face
challenges such as barren plateaus and complexity issues in large qubit states.
In contrast, our work overcomes these challenges, avoiding barren plateaus and
providing a practical solution for large qubit states. Our first contribution
offers a mathematical proof that the LQCNN structure preserves fundamental
properties. Furthermore, our second contribution analyzes the algorithm's
complexity, demonstrating its avoidance of barren plateaus through a structured
local cost function.
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