11,868 research outputs found
A Quantum Many-body Wave Function Inspired Language Modeling Approach
The recently proposed quantum language model (QLM) aimed at a principled
approach to modeling term dependency by applying the quantum probability
theory. The latest development for a more effective QLM has adopted word
embeddings as a kind of global dependency information and integrated the
quantum-inspired idea in a neural network architecture. While these
quantum-inspired LMs are theoretically more general and also practically
effective, they have two major limitations. First, they have not taken into
account the interaction among words with multiple meanings, which is common and
important in understanding natural language text. Second, the integration of
the quantum-inspired LM with the neural network was mainly for effective
training of parameters, yet lacking a theoretical foundation accounting for
such integration. To address these two issues, in this paper, we propose a
Quantum Many-body Wave Function (QMWF) inspired language modeling approach. The
QMWF inspired LM can adopt the tensor product to model the aforesaid
interaction among words. It also enables us to reveal the inherent necessity of
using Convolutional Neural Network (CNN) in QMWF language modeling.
Furthermore, our approach delivers a simple algorithm to represent and match
text/sentence pairs. Systematic evaluation shows the effectiveness of the
proposed QMWF-LM algorithm, in comparison with the state of the art
quantum-inspired LMs and a couple of CNN-based methods, on three typical
Question Answering (QA) datasets.Comment: 10 pages,4 figures,CIK
Neural Networks Architecture Evaluation in a Quantum Computer
In this work, we propose a quantum algorithm to evaluate neural networks
architectures named Quantum Neural Network Architecture Evaluation (QNNAE). The
proposed algorithm is based on a quantum associative memory and the learning
algorithm for artificial neural networks. Unlike conventional algorithms for
evaluating neural network architectures, QNNAE does not depend on
initialization of weights. The proposed algorithm has a binary output and
results in 0 with probability proportional to the performance of the network.
And its computational cost is equal to the computational cost to train a neural
network
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