Quantum Guru has been writing on different applications of Quantum Computing and here we once again touch upon a foreseeable Quantum Computing application, Quantum Natural Language Processing (QNLP). It is assumed that Quantum computing applications will be prevalent and applicable in our day-to-day life. Some of the potential applications of quantum computing are considered to be in the following domains:
- Artificial intelligence
- Logistics
- Machine learning
- Cryptography
- Genomics
- Optimization
Artificial Intelligence could reap substantial benefits of quantum computing. More specifically, it’s the Natural Language Processing (NLP) where quantum computing could have significant and even fundamental influence.
QNLP
Natural Language Processing (NLP) allows machines to break down and interpret human language. It’s at the core of tools we use every day – from translation software, chatbots, spam filters, and search engines, to grammar correction software, voice assistants, and social media monitoring tools. The compute speed for quantum computing is exponentially higher than that of classic computing. NLP could greatly exploit the increased computational speed for real world human-like experience. The hardware required for this kind of speed up will have to be quantum capable. Thus QNLP, though the research is still in infancy, aims at the development of NLP models explicitly designed to be executed on quantum hardware.
Categorical Compositional Distributional
Categorical compositional distributional semantics, known as is DisCoCat uses category theory to combine the benefits of two very different approaches to linguistics namely, categorical grammar and distributional semantics. First introduced in 2010, it’s a powerful mathematical model for composing the meaning of sentences in natural languages.
Two important QNLP resources are Quantinuum and Lambeq:
- Quantinuum – Honeywell Quantum Solutions and Cambridge Quantum have joined forces as Quantinuum to accelerate the delivery of real-world, quantum solutions. By uniting best-in-class software and enabling tools with the best-performing quantum computers, Quantinuum is delivering on the potential of quantum technology today. Quantinuum team majorly focuses on QNLP
- Lambeq – It is the world’s first high level Python library software for QNLP and is capable of converting the sentences into quantum circuits. Its motivation is to accelerate the development of practical and real world QNLP applications, such as chatbot, language translation, TTS, language generation, bioinformatics and text mining. Lambeq facilitates and automates the design and deployment of compositional-distributional (DisCo) NLP experiments, as described by CQ scientists. This deployment involves moving away from syntax/grammar diagrams, which encode a text’s structure, and toward TKET-implemented (classical) tensor networks or quantum circuits, which TKET can optimise for machine learning tasks like text categorisation. Furthermore, Lambeq is designed in a modular manner so that users can swap components in and out of the model and have architectural design flexibility.
Lambeq minimises the entry barrier for practitioners and researchers interested in AI and human-machine interactions, which could be one of quantum technology’s most important applications. TKET presently has a user base of hundreds of thousands of people all over the world. Lambeq has the potential to become an essential toolbox for the quantum computing community looking to engage with QNLP applications, which are one of AI’s most lucrative sectors. QNLP will apply to the study of symbol sequences that originate in genomes and proteomics, according to a critical point that has lately become clear.
Natural language generation
Another critical component for realizing QNLP is Natural language generation (NLG). NLG uses artificial intelligence (AI) programming to produce written or spoken narratives from a data set. Some of the applications of NLG are OCR, Speech recognition, Machine translation and Chatbots. NLG process consists of five steps as follows:
- Lexical analysis
- Parsing
- Semantic analysis
- Discourse integration
- Pragmatic analysis
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