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We build and analyse the network of 100 top-cited nodes (research papers and books from Google Scholar; the strength or citation of the nodes range from about 44 000 up to 100) starting in early 1980 until last year. These searched publications (papers and books) are based on Quantum Annealing Computation and Information categorized into four different sets: (A) Quantum/Transverse Field Spin Glass Model, (B) Quantum Annealing, (C) Quantum Adiabatic Computation and (D) Quantum Computation Information in the title or abstract of the searched publications. We fitted the growth in the annual number of publication ([Formula: see text]) in each of these four categories, A-D, to the form [Formula: see text] where [Formula: see text] denotes the time in years. We found the scaling time [Formula: see text] to be of the order of about 10 years for categories A and C, whereas [Formula: see text] is of the order of about 5 years for categories B and D. This article is part of the theme issue 'Quantum annealing and computation: challenges and perspectives'.
RESUMEN
We review the results for stochastic learning strategies, both classical (one-shot and iterative) and quantum (one-shot only), for optimizing the available many-choice resources among a large number of competing agents, developed over the last decade in the context of the Kolkata Paise Restaurant (KPR) Problem. Apart from few rigorous and approximate analytical results, both for classical and quantum strategies, most of the interesting results on the phase transition behavior (obtained so far for the classical model) uses classical Monte Carlo simulations. All these including the applications to computer science [job or resource allotments in Internet-of-Things (IoT)], transport engineering (online vehicle hire problems), operation research (optimizing efforts for delegated search problem, efficient solution of Traveling Salesman problem) will be discussed.
RESUMEN
We present here a somewhat personalized account of the emergence of econophysics as an attractive research topic in physical, as well as social, sciences. After a rather detailed storytelling about our endeavors from Kolkata, we give a brief description of the main research achievements in a simple and non-technical language. We also briefly present, in technical language, a piece of our recent research result. We conclude our paper with a brief perspective.
RESUMEN
A novel phase transition behavior is observed in the Kolkata Paise Restaurant problem where a large number (N) of agents or customers collectively (and iteratively) learn to choose among the N restaurants where she would expect to be alone that evening and would get the only dish available there (or may get randomly picked up if more than one agent arrive there that evening). The players are expected to evolve their strategy such that the publicly available information about past crowds in different restaurants can be utilized and each of them is able to make the best minority choice. For equally ranked restaurants, we follow two crowd-avoiding strategies: strategy I, where each of the ni(t) number of agents arriving at the ith restaurant on the tth evening goes back to the same restaurant the next evening with probability [ni(t)]-α, and strategy II, with probability p, when ni(t)>1. We study the steady state (t-independent) utilization fraction f:(1-f) giving the steady state (wastage) fraction of restaurants going without any customer at any particular evening. With both strategies, we find, near αc=0+ (in strategy I) or p=1- (in strategy II), the steady state wastage fraction (1-f)â(α-αc)ß or (pc-p)ß with ß≃0.8,0.87,1.0, and the convergence time τ [for f(t) becoming independent of t] varies as τâ(α-αc)-γ or (pc-p)-γ, with γ≃1.18,1.11,1.05 in infinite-dimensions (rest of the N-1 neighboring restaurants), three dimensions (six neighbors), and two dimensions (four neighbors), respectively.