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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