Demo: EQuaTE: Efficient Quantum Train Engine Design and Demonstration for Dynamic Software Analysis

被引：0
|
作者：
Park, Soohyun [1 ]
Feng, Hao [3 ]
Yun, Won Joon [1 ]
Park, Chanyoung [1 ]
Lee, Youn Kyu [2 ]
Jung, Soyi [4 ]
Kim, Joongheon [1 ]
机构：
[1] Korea Univ, Seoul, South Korea
[2] Hongik Univ, Seoul, South Korea
[3] Intel Labs, Hillsboro, OR USA
[4] Ajou Univ, Suwon, South Korea
来源：
2023 IEEE 43RD INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS, ICDCS | 2023年
关键词：
D O I：
10.1109/ICDCS57875.2023.00116
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper proposes an efficient quantum train engine (EQuaTE), a novel tool for quantum machine learning software which plots gradient variances to check whether our quantum neural network (QNN) falls into local minima (called barren plateaus in QNN). EQuaTE can be realized via dynamic analysis of the undetermined probabilistic qubit states. Furthermore, the proposed EQuaTE is capable of HCI-based visual feedback such that software engineers can recognize barren plateaus via visualization, allowing the modification of QNN based on this information.
引用
收藏
页码：1009 / 1012
页数：4
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