TY - JOUR

T1 - Quantum computations for disambiguation and question answering

AU - Correia, A. D.

AU - Moortgat, M.

AU - Stoof, H. T. C.

PY - 2022/3/30

Y1 - 2022/3/30

N2 - Automatic text processing is now a mature discipline in computer science, and so attempts at advancements using quantum computation have emerged as the new frontier, often under the term of quantum natural language processing. The main challenges consist in finding the most adequate ways of encoding words and their interactions on a quantum computer, considering hardware constraints, as well as building algorithms that take advantage of quantum architectures, so as to show improvement on the performance of natural language tasks. In this paper, we introduce a new framework that starts from a grammar that can be interpreted by means of tensor contraction, to build word representations as quantum states that serve as input to a quantum algorithm. We start by introducing an operator measurement to contract the representations of words, resulting in the representation of larger fragments of text. We then go on to develop pipelines for the tasks of sentence meaning disambiguation and question answering that take advantage of quantum features. For the first task, we show that our contraction scheme deals with syntactically ambiguous phrases storing the various different meanings in quantum superposition, a solution not available on a classical setting. For the second task, we obtain a question representation that contains all possible answers in equal quantum superposition, and we implement Grover's quantum search algorithm to find the correct answer, agnostic to the specific question, an implementation with the potential of delivering a result with quadratic speedup.

AB - Automatic text processing is now a mature discipline in computer science, and so attempts at advancements using quantum computation have emerged as the new frontier, often under the term of quantum natural language processing. The main challenges consist in finding the most adequate ways of encoding words and their interactions on a quantum computer, considering hardware constraints, as well as building algorithms that take advantage of quantum architectures, so as to show improvement on the performance of natural language tasks. In this paper, we introduce a new framework that starts from a grammar that can be interpreted by means of tensor contraction, to build word representations as quantum states that serve as input to a quantum algorithm. We start by introducing an operator measurement to contract the representations of words, resulting in the representation of larger fragments of text. We then go on to develop pipelines for the tasks of sentence meaning disambiguation and question answering that take advantage of quantum features. For the first task, we show that our contraction scheme deals with syntactically ambiguous phrases storing the various different meanings in quantum superposition, a solution not available on a classical setting. For the second task, we obtain a question representation that contains all possible answers in equal quantum superposition, and we implement Grover's quantum search algorithm to find the correct answer, agnostic to the specific question, an implementation with the potential of delivering a result with quadratic speedup.

KW - Grover's algorithm

KW - Quantum natural language processing

KW - Quantum search

KW - Question answering

KW - Syntactic ambiguities

UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=d7dz6a2i7wiom976oc9ff2iqvdhv8k5x&SrcAuth=WosAPI&KeyUT=WOS:000776203300004&DestLinkType=FullRecord&DestApp=WOS

U2 - 10.1007/s11128-022-03441-9

DO - 10.1007/s11128-022-03441-9

M3 - Article

SN - 1570-0755

VL - 21

JO - Quantum Information Processing

JF - Quantum Information Processing

IS - 4

M1 - 126

ER -