Image recognition with an adiabatic quantum computer I. Mapping to quadratic unconstrained binary optimization

Many artificial intelligence (AI) problems naturally map to NP-hard optimization problems. This has the interesting consequence that enabling human-level capability in machines often requires systems that can handle formally intractable problems. This issue can sometimes (but possibly not always) be resolved by building special-purpose heuristic algorithms, tailored to the problem in question. Because of the continued difficulties in automating certain tasks that are natural for humans, there remains a strong motivation for AI researchers to investigate and apply new algorithms and techniques to hard AI problems. Recently a novel class of relevant algorithms that require quantum mechanical hardware have been proposed. These algorithms, referred to as quantum adiabatic algorithms, represent a new approach to designing both complete and heuristic solvers for NP-hard optimization problems. In this work we describe how to formulate image recognition, which is a canonical NP-hard AI problem, as a Quadratic Unconstrained Binary Optimization (QUBO) problem. The QUBO format corresponds to the input format required for D-Wave superconducting adiabatic quantum computing (AQC) processors.Comment: 7 pages, 3 figure

A log square average case algorithm to make insertions in fast similarity search

To speed up similarity based searches many indexing techniques have been proposed in order to address the problem of efficiency. However, most of the proposed techniques do not admit fast insertion of new elements once the index is built. The main effect is that changes in the environment are very costly to be taken into account. In this work, we propose a new technique to allow fast insertions of elements in a family of static tree-based indexes. Unlike other techniques, the resulting index is exactly equal to the index that would be obtained by building it from scratch. Therefore there is no performance degradation in search time. We show that the expected number of distance computations (and the average time complexity) is bounded by a function that grows with log2(n) where n is the size of the database. In order to check the correctness of our approach some experiments with artificial and real data are carried out.This work has been supported in part by Grants TIN2009-14205-C04-01 from the Spanish CICYT (Ministerio de Ciencia e Innovación), the IST Programme of the European Community, under the Pascal Network of Excellence, IST-2002-506778, and the program CONSOLIDER INGENIO 2010 (CSD2007-00018)

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition