A hybrid classical-quantum workflow for natural language processing
arXiv:2004.06800 · doi:10.1088/2632-2153/abbd2e
Abstract
Natural language processing (NLP) problems are ubiquitous in classical computing, where they often require significant computational resources to infer sentence meanings. With the appearance of quantum computing hardware and simulators, it is worth developing methods to examine such problems on these platforms. In this manuscript we demonstrate the use of quantum computing models to perform NLP tasks, where we represent corpus meanings, and perform comparisons between sentences of a given structure. We develop a hybrid workflow for representing small and large scale corpus data sets to be encoded, processed, and decoded using a quantum circuit model. In addition, we provide our results showing the efficacy of the method, and release our developed toolkit as an open software suite.
arXiv commentFor associated code, see https://github.com/ICHEC/QNLP
References in corpus (17)
- SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
- Efficient Estimation of Word Representations in Vector Space
- Distributed Representations of Words and Phrases and their Compositionality
- The NumPy array: a structure for efficient numerical computation
- Elementary gates for quantum computation
- Quantum random access memory
- Interacting Quantum Observables: Categorical Algebra and Diagrammatics
- Probabilistic Quantum Memories
- On the robustness of bucket brigade quantum RAM
- qHiPSTER: The Quantum High Performance Software Testing Environment
- Intel Quantum Simulator: A cloud-ready high-performance simulator of quantum circuits
- Mathematical Foundations for a Compositional Distributional Model of Meaning
- Quantum Analog-Digital Conversion
- Quantum Algorithms for Compositional Natural Language Processing
- On the qubit routing problem
- Quantum Language Processing
- Meaning-focused and Quantum-inspired Information Retrieval