lambeq: An Efficient High-Level Python Library for Quantum NLP
원문 초록
We present lambeq, the first high-level Python library for Quantum Natural Language Processing (QNLP). The open-source toolkit offers a detailed hierarchy of modules and classes implementing all stages of a pipeline for converting sentences to string diagrams, tensor networks, and quantum circuits ready to be used on a quantum computer. lambeq supports syntactic parsing, rewriting and simplification of string diagrams, ansatz creation and manipulation, as well as a number of compositional models for preparing quantum-friendly representations of sentences, employing various degrees of syntax sensitivity. We present the generic architecture and describe the most important modules in detail, demonstrating the usage with illustrative examples. Further, we test the toolkit in practice by using it to perform a number of experiments on simple NLP tasks, implementing both classical and quantum pipelines.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
A tensor network approach for chaotic time series prediction
Quantum Methods for Managing Ambiguity in Natural Language Processing
tn4ml: Tensor Network Training and Customization for Machine Learning
Comb Tensor Networks vs. Matrix Product States: Enhanced Efficiency in High-Dimensional Spaces
LTNtorch: PyTorch Implementation of Logic Tensor Networks
Learning out-of-time-ordered correlators with classical kernel methods
토론
이 논문과 연결된 포럼 글이 아직 없습니다.