Development of research network on Quantum Annealing Computation and Information using Google Scholar data

Abstract

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 ( n p ) in each of these four categories, A–D, to the form n p ∼ exp ⁡ ( t / τ ) , where t denotes the time in years. We found the scaling time τ to be of the order of about 10 years for categories A and C, whereas τ 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’.

Quantum annealing and computation: challenges and perspectives

“…The development of quantum annealing and adiabatic quantum computers has been unique in physics of many-body quantum systems as well as in quantum engineering of information processing and has become a very active field of research (see e.g. [ 30 ] in this issue, showing that the research publications in this field are growing every 5 years by a factor (Euler number ), compared to some other very active contemporary research fields taking almost double that time to grow by the same factor). We hope the issue will provide useful study materials and will be sufficiently informative to the interested researchers, younger ones in particular.…”

“…The paper entitled 'Development of research network on quantum annealing, computation and information using Google Scholar data' [30] by Sinha analyses the time evolution of…”

“…The paper entitled ‘Development of research network on quantum annealing, computation and information using Google Scholar data’ [ 30 ] by Sinha analyses the time evolution of research networks by extracting data on papers related to quantum annealing and quantum computation using Google Scholar.…”

Optimization Applications as Quantum Performance Benchmarks

“…The vast majority of published benchmarking work in QA considers application-level tests [3,[53][54][55] using models with more than 100 qubits, which presents significant challenges for validation by comparison to the classical simulation of ideal (noise-free) quantum systems. Consequently, the bulk of this application benchmarking work compares the performance of quantum annealing hardware to classical methods, like Simulated Annealing [56], Parallel Tempering [57,58] and Integer Programming [3,59].…”