Grammar-Aware Question-Answering on Quantum Computers

• URL: http://arxiv.org/abs/2012.03756v1
• Date: Mon, 7 Dec 2020 14:49:34 GMT
• Title: Grammar-Aware Question-Answering on Quantum Computers
• Authors: Konstantinos Meichanetzidis, Alexis Toumi, Giovanni de Felice, Bob Coecke
• Abstract summary: We perform the first implementation of an NLP task on noisy intermediate-scale quantum (NISQ) hardware. We encode word-meanings in quantum states and we explicitly account for grammatical structure. Our novel QNLP model shows concrete promise for scalability as the quality of the quantum hardware improves.
• Score: 0.17205106391379021
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Natural language processing (NLP) is at the forefront of great advances in contemporary AI, and it is arguably one of the most challenging areas of the field. At the same time, with the steady growth of quantum hardware and notable improvements towards implementations of quantum algorithms, we are approaching an era when quantum computers perform tasks that cannot be done on classical computers with a reasonable amount of resources. This provides a new range of opportunities for AI, and for NLP specifically. Earlier work has already demonstrated a potential quantum advantage for NLP in a number of manners: (i) algorithmic speedups for search-related or classification tasks, which are the most dominant tasks within NLP, (ii) exponentially large quantum state spaces allow for accommodating complex linguistic structures, (iii) novel models of meaning employing density matrices naturally model linguistic phenomena such as hyponymy and linguistic ambiguity, among others. In this work, we perform the first implementation of an NLP task on noisy intermediate-scale quantum (NISQ) hardware. Sentences are instantiated as parameterised quantum circuits. We encode word-meanings in quantum states and we explicitly account for grammatical structure, which even in mainstream NLP is not commonplace, by faithfully hard-wiring it as entangling operations. This makes our approach to quantum natural language processing (QNLP) particularly NISQ-friendly. Our novel QNLP model shows concrete promise for scalability as the quality of the quantum hardware improves in the near future.

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