8,034 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
A quasi-current representation for information needs inspired by Two-State Vector Formalism
Recently, a number of quantum theory (QT)-based information retrieval (IR) models have been proposed for modeling session search task that users issue queries continuously in order to describe their evolving information needs (IN). However, the standard formalism of QT cannot provide a complete description for usersâ current IN in a sense that it does not take the âfutureâ information into consideration. Therefore, to seek a more proper and complete representation for usersâ IN, we construct a representation of quasi-current IN inspired by an emerging Two-State Vector Formalism (TSVF). With the enlightenment of the completeness of TSVF, a âtwo-state vectorâ derived from the âfutureâ (the current query) and the âhistoryâ (the previous query) is employed to describe usersâ quasi-current IN in a more complete way. Extensive experiments are conducted on the session tracks of TREC 2013 & 2014, and show that our model outperforms a series of compared IR models
Complex Networks from Classical to Quantum
Recent progress in applying complex network theory to problems in quantum
information has resulted in a beneficial crossover. Complex network methods
have successfully been applied to transport and entanglement models while
information physics is setting the stage for a theory of complex systems with
quantum information-inspired methods. Novel quantum induced effects have been
predicted in random graphs---where edges represent entangled links---and
quantum computer algorithms have been proposed to offer enhancement for several
network problems. Here we review the results at the cutting edge, pinpointing
the similarities and the differences found at the intersection of these two
fields.Comment: 12 pages, 4 figures, REVTeX 4-1, accepted versio
Looking at Vector Space and Language Models for IR using Density Matrices
In this work, we conduct a joint analysis of both Vector Space and Language
Models for IR using the mathematical framework of Quantum Theory. We shed light
on how both models allocate the space of density matrices. A density matrix is
shown to be a general representational tool capable of leveraging capabilities
of both VSM and LM representations thus paving the way for a new generation of
retrieval models. We analyze the possible implications suggested by our
findings.Comment: In Proceedings of Quantum Interaction 201
CNM: An Interpretable Complex-valued Network for Matching
This paper seeks to model human language by the mathematical framework of
quantum physics. With the well-designed mathematical formulations in quantum
physics, this framework unifies different linguistic units in a single
complex-valued vector space, e.g. words as particles in quantum states and
sentences as mixed systems. A complex-valued network is built to implement this
framework for semantic matching. With well-constrained complex-valued
components, the network admits interpretations to explicit physical meanings.
The proposed complex-valued network for matching (CNM) achieves comparable
performances to strong CNN and RNN baselines on two benchmarking question
answering (QA) datasets
A Quantum Query Expansion Approach for Session Search
Recently, Quantum Theory (QT) has been employed to advance the theory of Information Retrieval (IR). Various analogies between QT and IR have been established. Among them, a typical one is applying the idea of photon polarization in IR tasks, e.g., for document ranking and query expansion. In this paper, we aim to further extend this work by constructing a new superposed state of each document in the information need space, based on which we can incorporate the quantum interference idea in query expansion. We then apply the new quantum query expansion model to session search, which is a typical Web search task. Empirical evaluation on the large-scale Clueweb12 dataset has shown that the proposed model is effective in the session search tasks, demonstrating the potential of developing novel and effective IR models based on intuitions and formalisms of QT
A Survey of Quantum Theory Inspired Approaches to Information Retrieval
Since 2004, researchers have been using the mathematical framework of Quantum Theory (QT) in Information Retrieval (IR). QT offers a generalized probability and logic framework. Such a framework has been shown capable of unifying the representation, ranking and user cognitive aspects of IR, and helpful in developing more dynamic, adaptive and context-aware IR systems. Although Quantum-inspired IR is still a growing area, a wide array of work in different aspects of IR has been done and produced promising results. This paper presents a survey of the research done in this area, aiming to show the landscape of the field and draw a road-map of future directions
Quantum-inspired Machine Learning on high-energy physics data
Tensor Networks, a numerical tool originally designed for simulating quantum
many-body systems, have recently been applied to solve Machine Learning
problems. Exploiting a tree tensor network, we apply a quantum-inspired machine
learning technique to a very important and challenging big data problem in high
energy physics: the analysis and classification of data produced by the Large
Hadron Collider at CERN. In particular, we present how to effectively classify
so-called b-jets, jets originating from b-quarks from proton-proton collisions
in the LHCb experiment, and how to interpret the classification results. We
exploit the Tensor Network approach to select important features and adapt the
network geometry based on information acquired in the learning process.
Finally, we show how to adapt the tree tensor network to achieve optimal
precision or fast response in time without the need of repeating the learning
process. These results pave the way to the implementation of high-frequency
real-time applications, a key ingredient needed among others for current and
future LHCb event classification able to trigger events at the tens of MHz
scale.Comment: 13 pages, 4 figure
Synthesis of time-to-amplitude converter by mean coevolution with adaptive parameters
Copyright © 2011 the authors and Scientific Research Publishing Inc. This work is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)The challenging task to synthesize automatically a time-to-amplitude converter, which unites by its functionality several digital circuits, has been successfully solved with the help of a novel methodology. The proposed approach is based on a paradigm according to which the substructures are regarded as additional mutation types and when ranged with other mutations form a new adaptive individual-level mutation technique. This mutation approach led to the discovery of an original coevolution strategy that is characterized by very low selection rates. Parallel island-model evolution has been running in a hybrid competitive-cooperative interaction throughout two incremental stages. The adaptive population size is applied for synchronization of the parallel evolutions
- âŠ