A Model for Circuit Execution Runtime And Its Implications for Quantum Kernels At Practical Data Set Sizes

Quantum machine learning (QML) is a fast-growing discipline within quantum computing. One popular QML algorithm, quantum kernel estimation, uses quantum circuits to estimate a similarity measure (kernel) between two classical feature vectors. Given a set of such circuits, we give a heuristic, predictive model for the total circuit execution time required, based on a recently-introduced measure of the speed of quantum computers. In doing so, we also introduce the notion of an "effective number of quantum volume layers of a circuit", which may be of independent interest. We validate the performance of this model using synthetic and real data by comparing the model's predictions to empirical runtime data collected from IBM Quantum computers through the use of the Qiskit Runtime service. At current speeds of today's quantum computers, our model predicts data sets consisting of on the order of hundreds of feature vectors can be processed in order a few hours. For a large-data workflow, our model's predictions for runtime imply further improvements in the speed of circuit execution -- as well as the algorithm itself -- are necessary.Comment: 8.5 pages of main text + 1.5 pages of appendices. 7 figures & 3 table

Editorial Challenge: From a Quarterly to a Bimonthly Journal

Starting with issue 4 of volume 7(2012) International Journal of Computers Communications & Control (INT J COMPUT COMMUN, IJCCC) [4] is a member of, and subscribes to the principles of, the Committee on Publication Ethics (COPE) [2].Beginning with issue 1 of volume 8(2013) IJCCC will be published as a bimonthly journal (6 issues/year) [5]

Development of Finite Volume Algorithm on Parallel Computer for Prediction of Three-Dimensional Turbulent Compressible Flows

This project aims at developing a general purpose, user-friendly Computer code for numerical prediction of three dimensional, turbulent, compressible flows solving the time-averaged, Navier Stokes equations in body-fitted non-orthogonal coordinate system . A finite volume method [5] has already been developed by the present investigator and others for incompressible flows employing the concept of the Semi-IMplicit Pressure Linked Equations (SIMPLE) of Patankar & Spalding [6], revised for cell-centred variable arrangement and using Cartesian Velocity Components as dependent variables . The same method is proposed to be modified to account for the compressibility effects and hence to achieve a unified aproach for incompressible and compressible flows . Turbulence will be simulated through the Eddy-Viscosity based two equations (K- E) models on which the present investigator has already gained considerable experience [7 - 101 for both 2D & 3D incompressible flows . In order to meet the large Computer storage and CPU demand for real life 3D problems a parallelised version of the code will be generated, compitable to the in-house MK-II FLOSOLVER at NAL for which however the Computer resources and specially the hardware need to be augmented . How to handle irregular flow geometries, modelling turbulence for 3D separated flows and finally the effective utilisation of parallel computers for large CPU and storage-consuming computer codes form the three-major problems to be studied under this research project

Aspects of Computer Use by Chemistry Students in Their Final Year at School

This report is an analysis of responses to a questionnaire given to freshers arriving at the Chemistry Department of Liverpool University to determine their previous use of computers, their perceptions of their competence, confidence and ability to benefit from computers and the importance they attach to possessing a computer and being trained in the use of computers. The questionnaire has been used between 1990 and 1998; the results presented herein include results from the surveys between 1996 and 1998. The 1990 data have been previously presented in Software Reviews Volume 3, issued February 1991; the 1992 data can be found in Volume 7, dated January 1993; the 1993 data are in Volume 9, dated March 1994; the 1994 data in Volume 11, dated May 1995; and the 1995 data in Volume 12, dated October 1995. The data for 1996 and 1997 have been reported, in part, in a comparison of results for a cohort of students who were freshers in 1996 and who returned to university in 1997. This report has been published on our web site at http://www.liv.ac.uk/ctichem/surv9697.html. The survey form asks students about their use of computers in their last year at school, their perceptions of their computing skills, confidence, expectations of ability and interest in using computers, whether or not they own or have access to a computer and their attitudes to buying or hiring a computer and to training (even if that were given out of hours). The form also allows for comments. The 1996 survey data is based upon returns from 60 males and 32 females, the 1997 data on 52 males and 34 females, the 1998 data on 53 males and 27 females

Documenting and predicting topic changes in Computers in Biology and Medicine: A bibliometric keyword analysis from 1990 to 2017

The Computers in Biology and Medicine (CBM) journal promotes the use of com-puting machinery in the fields of bioscience and medicine. Since the first volume in 1970, the importance of computers in these fields has grown dramatically, this is evident in the diversification of topics and an increase in the publication rate. In this study, we quantify both change and diversification of topics covered in CBM. This is done by analysing the author supplied keywords, since they were electronically captured in 1990. The analysis starts by selecting 40 keywords, related to Medical (M) (7), Data (D)(10), Feature (F) (17) and Artificial Intelligence (AI) (6) methods. Automated keyword clustering shows the statistical connection between the selected keywords. We found that the three most popular topics in CBM are: Support Vector Machine (SVM), Elec-troencephalography (EEG) and IMAGE PROCESSING. In a separate analysis step, we bagged the selected keywords into sequential one year time slices and calculated the normalized appearance. The results were visualised with graphs that indicate the CBM topic changes. These graphs show that there was a transition from Artificial Neural Network (ANN) to SVM. In 2006 SVM replaced ANN as the most important AI algo-rithm. Our investigation helps the editorial board to manage and embrace topic change. Furthermore, our analysis is interesting for the general reader, as the results can help them to adjust their research directions

ANALISIS KESULITAN SISWA SMP DALAM MATERI BANGUN RUANG SISI DATAR BERBANTUAN GOOGLE CLASSROOM

This research describes students' difficulties in learning mathematics, the research was conducted at SMPN 1 CIHAMPELAS class IX with the material  build  room  side  flat  assisted by google classroom. The approach  in this research  uses a  qualitative  approach. The instrument used is a description test which amounts to 7 questions with different difficulty levels collected through google classroom. Google Classroom is a learning application released by Google. The ease of access via computers and mobile phones is very beneficial for teachers and students in schools. The goal is to describe blended learning, google classroom as an alternative in learning, and blended learning through google classroom. difficulty in calculating the surface area of a cube, as many as 33.34% of students have difficulty in calculating the surface area of a prism, as many as 33.60% of students have difficulty in calculating the surface area of a pyramid, as many as 11.12% of students have difficulty in calculating the volume of a cuboid, 20.75% of students still have difficulty in calculating the volume of a prism, and as many as 57.78% of students had difficulty solving problems involving the diagonal of the space, the diagonal of the plane, and the diagonal of the plane