RESEARCH PAPERS

By Tim Jaschek, Marko Bucyk, & Jaspreet S. Oberoi Clustering, or grouping, dataset elements based on similarity can be used not only to classify a dataset into a few categories, but also to approximate it by a relatively large number of representative elements. In the latter scenario, referred to as extreme clustering, datasets are enormous and the number of representative clusters is large....

By Nicolas Pradignac, Maliheh Aramon, & Helmut G. Katzgraber The feasibility pump algorithm is an efficient primal heuristic for finding feasible solutions to mixed-integer programming problems. The algorithm suffers mainly from fast convergence to local optima. In this paper, we investigate the effect of an alternative approach to circumvent this challenge by designing a two-stage approach...

By Roohollah Ghobadi, Jaspreet S. Oberoi, & Ehsan Zahedinejhad Kernel methods are used extensively in classical machine learning, especially in the field of pattern analysis. In this paper, we propose a kernel-based quantum machine learning algorithm that can be implemented on a near-term, intermediate scale quantum device. Our proposal is based on estimating classically intractable kernel...

By Maritza Hernandez, Guo Liang Gan, Kirby Linvill, Carl Dukatz, Jun Feng, & Govinda Bhisetti Measuring similarity between molecules is an important part of virtual screening (VS) experiments deployed during the early stages of drug discovery. Most widely used methods for evaluating the similarity of molecules use molecular fingerprints to encode structural information. While similarity...

By Ehsan Zahedinejad, Daniel Crawford, Clemens Adolphs, & Jaspreet S. Oberoi Signed graphs serve as a primary tool for modelling social networks. They can represent relationships between individuals (i.e., nodes) with the use of signed edges. Finding communities in a signed graph is of great importance in many areas, for example, targeted advertisement. We propose an algorithm to detect...

By Dominic Marchand, Moslem Noori, Austin Roberts, Gili Rosenberg, Brad Woods, Ugur Yildiz, Marc Coons, David Devore, & Peter Margl Discovering the low-energy conformations of a molecule is of great interest to computational chemists, with applications in in silico materials design and drug discovery. In this paper, we propose a variable neighbourhood search heuristic for the conformational...

By Shunji Matsuura, Takeshi Yamazaki, Valentin Senicourt, & Arman Zaribafiyan The accelerated progress in manufacturing noisy intermediate-scale quantum (NISQ) computing hardware has opened the possibility of exploring its application in transforming approaches to solving computationally challenging problems. The important limitations common among all NISQ computing technologies are the...

By Behrooz Sepehry, Ehsan Iranmanesh, Michael P. Friedlander, & Pooya Ronagh We introduce two quantum algorithms for solving structured prediction problems. We show that a stochastic subgradient descent method that uses the quantum minimum finding algorithm and takes its probabilistic failure into account solves the structured prediction problem with a runtime that scales with the square...

By Maliheh Aramon, Gili Rosenberg, Elisabetta Valiante, Toshiyuki Miyazawa, Hirotaka Tamura, & Helmut G. Katzgraber The Fujitsu Digital Annealer (DA) is designed to solve fully connected quadratic unconstrained binary optimization (QUBO) problems. It is implemented on application-specific CMOS hardware and currently solves problems of up to 1024 variables. The DA's algorithm is currently...

By Takeshi Yamazaki, Shunji Matsuura, Ali Narimani, Anushervon Saidmuradov, & Arman Zaribafiyan With the aim of establishing a framework to efficiently perform the practical application of quantum chemistry simulation on near-term quantum devices, we envision a hybrid quantum–classical framework for leveraging problem decomposition (PD) techniques in quantum chemistry. Specifically, we use...