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A quantum teleportation inspired algorithm produces sentence meaning from word meaning and grammatical structure 15

S. Twareque Ali, S. Hasibul, Hassan Chowdhury, Stephen Charles Clark, Bob Coecke, Edward Grefenstette, Mehrnoosh Sadrzadeh, Kazuo Fujikawa, Saeid Molladavoudi

Published Jan 1, 2014• 2 citations
OpenAlex

Abstract

We discuss an algorithm which produces the meaning of a sentence given meanings of its words, and its resemblance to quantum teleportation. In fact, this protocol was the main source of inspiration for this algorithm which has many applications in the area of Natural Language Processing.

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A quantum teleportation inspired algorithm produces sentence meaning from word meaning and grammatical structure

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IXPUG 2019 Annual Conference at CERN

Myles Doyle

<!--HTML-->Natural language processing (NLP) is often used to perform tasks like sentiment analysis, relationship extraction and word sense disambiguation. Most traditional NLP algorithms operate over strings of words and are limited since they analyse meanings of the component words in a corpus without information about grammatical rules of the language. Consequently, the qualities of results of these traditional algorithms are often unsatisfactory with increase in problem complexity. An alternate approach called “compositional semantics” incorporates the grammatical structure of sentences in a language into the analysis algorithms. One such model is “distributional compositional semantics” (DisCo) which gives grammatically informed algorithms that compute the meaning of sentences. This algorithm has been noted to offer significant improvements to the quality of results. However, the main challenge in its implementation is the need for large classical computational resources. The DisCo model was developed by its authors with direct inspiration from quantum theory, and presents two quantum algorithms: the “closest vector problem” algorithm and the “CSC sentence similarity” algorithm. Their quantum implementation lowers storage and compute requirements compared to a classic HPC implementation. In this project, the Irish Centre for High-End Computing collaborates with Intel Corporation to implement the two DisCo model quantum algorithms on the Intel Quantum Simulator (Intel-QS) deployed on the Irish national supercomputer. The Intel-QS performs a number of single- and multi-node optimizations, including vectorization, multi-threading, cache blocking, as well as overlapping computation with communication. In this project, we target improving the scalability of Intel-QS beyond the limitations imposed by standard MPI implementations and target corpuses with ~1000 most common words using up to 36 qubits simulation. The implemented solution will be able to compute the meanings of two sentences (built from words in the corpus) and decide if their meanings match.

•Jan 1, 2019•0 citations
The main goal of this master's thesis is to introduce Quantum Natural Language Processing (QNLP) in a way understandable by both the NLP engineer and the quantum computing practitioner. QNLP is a recent application of quantum computing that aims at representing sentences' meaning as vectors encoded into quantum computers. To achieve this, the distributional meaning of words is extended by the compositional meaning of sentences (DisCoCat model) : the vectors representing words' meanings are composed through the syntactic structure of the sentence. This is done using an algorithm based on tensor products. We see that this algorithm is inefficient on classical computers but scales well using quantum circuits. After exposing the practical details of its implementation, we go through three use-cases.
Abstract: Quantum Natural Language Processing (QNLP) represents a pioneering approach to understanding and analyzing natural language by leveraging the principles of quantum computing. This project aims to explore the semantic analysis of autogenerated sentences using QNLP techniques. Traditional Natural Language Processing (NLP) methods have achieved significant milestones in language understanding and generation; however, they often struggle with the intricacies of context, ambiguity, and the vast computational resources required for complex tasks. QNLP offers a novel paradigm by utilizing the superposition and entanglement properties of quantum states, potentially revolutionizing how semantic meaning is extracted and processed. The primary objective of this project is to develop a QNLP framework capable of performing semantic analysis on autogenerated sentences. We will investigate the application of quantum algorithms and quantum machine learning models to decode the semantic structure and context of sentences generated by language models. This involves encoding textual data into quantum states, applying quantum gates and circuits to manipulate these states, and measuring the outcomes to interpret semantic information. By integrating quantum computing with NLP, this project seeks to achieve higher accuracy and efficiency in understanding complex language patterns. The anticipated outcomes include a comprehensive evaluation of QNLP's performance in semantic analysis, a comparison with classical NLP methods, and potential advancements in language processing tasks such as sentiment analysis, language translation, and text summarization. This project will contribute to the growing body of research at the intersection of quantum computing and artificial intelligence, paving the way for innovative solutions in the field of natural language processing.
Recent research has investigated quantum NLP, designing algorithms that process natural language in quantum computers, and also quantum-inspired algorithms that improve NLP performance on classical computers.In this survey, we review representative methods at the intersection of NLP and quantum physics in the past ten years, categorizing them according to the use of quantum theory, the linguistic targets that are modeled, and the downstream application.The literature review ends with a discussion on the key factors to the success that has been achieved by existing work, as well as challenges ahead, with the goal of better understanding the promises and further directions.
<!--HTML-->Natural language processing (NLP) is often used to perform tasks like sentiment analysis, relationship extraction and word sense disambiguation. Most traditional NLP algorithms operate over strings of words and are limited since they analyse meanings of the component words in a corpus without information about grammatical rules of the language. Consequently, the qualities of results of these traditional algorithms are often unsatisfactory with increase in problem complexity. An alternate approach called “compositional semantics” incorporates the grammatical structure of sentences in a language into the analysis algorithms. One such model is “distributional compositional semantics” (DisCo) which gives grammatically informed algorithms that compute the meaning of sentences. This algorithm has been noted to offer significant improvements to the quality of results. However, the main challenge in its implementation is the need for large classical computational resources. The DisCo model was developed by its authors with direct inspiration from quantum theory, and presents two quantum algorithms: the “closest vector problem” algorithm and the “CSC sentence similarity” algorithm. Their quantum implementation lowers storage and compute requirements compared to a classic HPC implementation. In this project, the Irish Centre for High-End Computing collaborates with Intel Corporation to implement the two DisCo model quantum algorithms on the Intel Quantum Simulator (Intel-QS) deployed on the Irish national supercomputer. The Intel-QS performs a number of single- and multi-node optimizations, including vectorization, multi-threading, cache blocking, as well as overlapping computation with communication. In this project, we target improving the scalability of Intel-QS beyond the limitations imposed by standard MPI implementations and target corpuses with ~1000 most common words using up to 36 qubits simulation. The implemented solution will be able to compute the meanings of two sentences (built from words in the corpus) and decide if their meanings match.