使用量子启发的张量网络进行序列处理
Carys Harvey1,2, Richie Yeung3,4, Konstantinos Meichanetzidis3
1Quantinuum, 17 Beaumont Street, Oxford, UK. carys.harvey@bristol.ac.uk.
Scientific reports
|February 28, 2025
概括
我们开发了高效的张量网络模型用于序列处理,灵感来自量子计算. 这些模型改善了生物信息学和自然语言处理中的分类任务,证明了近期量子设备的潜力.
科学领域:
- 人工智能的人工智能
- 量子计算是一种量子计算.
- 计算语言学 计算语言学
背景情况:
- 张量网络模型为序列处理任务提供了一个有希望的途径.
- 概率图形模型为理解复杂数据结构提供了基础.
- 量子计算为机器学习提供了新的计算范式.
研究的目的:
- 为序列处理引入高效的张量网络模型.
- 为了利用诱导偏差来改进网络架构和数据相关性.
- 探索这些模型在近期量子处理器上的潜力.
主要方法:
- 使用复杂和单元张量来创建表达性网络.
- 通过网络架构引入诱导偏差,以实现相关性和组合性.
- 代表模型作为参数化量子电路用于物理过程描述.
主要成果:
- 实现了对数树幅,以提高可训练性和高效收缩.
- 在生物信息学和自然语言处理中证明了成功的二进制分类.
- 在Quantinuum的H2-1捕获离子量子处理器上实现模型.
结论:
- 张量网络模型为在NLP中探索张量结构和语法先验提供了一个可扩展的方法.
- 模型对量子比特进行操作映射,可以通过量子测量进行无偏向的采样.
- 近期的量子设备显示出执行这些先进的机器学习模型的潜力.
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