8,290 research outputs found
Quantum Advantage in Information Retrieval
Random access codes have provided many examples of quantum advantage in
communication, but concern only one kind of information retrieval task. We
introduce a related task - the Torpedo Game - and show that it admits greater
quantum advantage than the comparable random access code. Perfect quantum
strategies involving prepare-and-measure protocols with experimentally
accessible three-level systems emerge via analysis in terms of the discrete
Wigner function. The example is leveraged to an operational advantage in a
pacifist version of the strategy game Battleship. We pinpoint a characteristic
of quantum systems that enables quantum advantage in any bounded-memory
information retrieval task. While preparation contextuality has previously been
linked to advantages in random access coding we focus here on a different
characteristic called sequential contextuality. It is shown not only to be
necessary and sufficient for quantum advantage, but also to quantify the degree
of advantage. Our perfect qutrit strategy for the Torpedo Game entails the
strongest type of inconsistency with non-contextual hidden variables, revealing
logical paradoxes with respect to those assumptions.Comment: 15 pages, 11 figures; new presentation, additional figures and
reference
Quantum theory-inspired search
With the huge number and diversity of the users, the advertising products and services, the rapid growth of online multimedia resources, the context of information needs are even more broad and complex. Although research in search engine technology has led to various models over the past three decades, the investigation for effectively integrating the dimensions of context to deploy advanced search technology has been limited due to the lack of a unified modeling and evaluation framework. Quantum Theory (QT) has created new and unprecedented means for communicating and computing. Besides computer science, optics, electronics, physics, QT and search engine technology can be combined: interference in user interaction; entanglement in cognition; superposition in word meaning; non-classical probability in information ranking; complex vector spaces in multimedia search. This paper highlights our recent results on QT-inspired search engine technology
mARC: Memory by Association and Reinforcement of Contexts
This paper introduces the memory by Association and Reinforcement of Contexts
(mARC). mARC is a novel data modeling technology rooted in the second
quantization formulation of quantum mechanics. It is an all-purpose incremental
and unsupervised data storage and retrieval system which can be applied to all
types of signal or data, structured or unstructured, textual or not. mARC can
be applied to a wide range of information clas-sification and retrieval
problems like e-Discovery or contextual navigation. It can also for-mulated in
the artificial life framework a.k.a Conway "Game Of Life" Theory. In contrast
to Conway approach, the objects evolve in a massively multidimensional space.
In order to start evaluating the potential of mARC we have built a mARC-based
Internet search en-gine demonstrator with contextual functionality. We compare
the behavior of the mARC demonstrator with Google search both in terms of
performance and relevance. In the study we find that the mARC search engine
demonstrator outperforms Google search by an order of magnitude in response
time while providing more relevant results for some classes of queries
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
Evolutionary Subject Tagging in the Humanities; Supporting Discovery and Examination in Digital Cultural Landscapes
In this paper, the authors attempt to identify problematic issues for subject tagging in the humanities, particularly those associated with information objects in digital formats. In the third major section, the authors identify a number of assumptions that lie behind the current practice of subject classification that we think should be challenged. We move then to propose features of classification systems that could increase their effectiveness. These emerged as recurrent themes in many of the conversations with scholars, consultants, and colleagues. Finally, we suggest next steps that we believe will help scholars and librarians develop better subject classification systems to support research in the humanities.NEH Office of Digital Humanities: Digital Humanities Start-Up Grant (HD-51166-10
Towards memory supporting personal information management tools
In this article we discuss re-retrieving personal information objects and relate the task to recovering from lapse(s) in memory. We propose that fundamentally it is lapses in memory that impede users from successfully re-finding the information they need. Our hypothesis is that by learning more about memory lapses in non-computing contexts and how people cope and recover from these lapses, we can better inform the design of PIM tools and improve the user's ability to re-access and re-use objects. We describe a diary study that investigates the everyday memory problems of 25 people from a wide range of backgrounds. Based on the findings, we present a series of principles that we hypothesize will improve the design of personal information management tools. This hypothesis is validated by an evaluation of a tool for managing personal photographs, which was designed with respect to our findings. The evaluation suggests that users' performance when re-finding objects can be improved by building personal information management tools to support characteristics of human memory
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