RESEARCH PAPERS

Selected research papers published by 1QBit’s team and collaborators.

The Power of One Qubit in Machine Learning

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...

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A Quantum-Inspired Method for Three-Dimensional Ligand-Based Virtual Screening

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...

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Multi-Community Detection in Signed Graphs Using Quantum Hardware

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...

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A Variable Neighbourhood Descent Heuristic for Conformational Search Using a Quantum Annealer

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...

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VanQver: The Variational and Adiabatically Navigated Quantum Eigensolver

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...

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Smooth Structured Prediction Using Quantum and Classical Gibbs Samplers

By Behrooz Sepehry, Ehsan Iranmanesh, Michael P. Friedlander, & Pooya Ronagh We introduce a quantum algorithm for solving structured prediction problems with a runtime that scales with the square root of the size of the label space, but scales in \(\widetilde O\left(\epsilon^{-3.5}\right)\) with respect to the precision, \(\epsilon\), of the solution. In doing so, we analyze a stochastic...

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Towards the Practical Application of Near-Term Quantum Computers in Quantum Chemistry Simulations: A Problem Decomposition Approach

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...

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Combinatorial Optimization on Gate Model Quantum Computers: A Survey

By Ehsan Zahedinejad & Arman Zaribafiyan The advent of quantum computing processors with the possibility to scale beyond experimental capacities magnifies the importance of studying their applications. Combinatorial optimization problems can be one of the promising applications of these new devices. These problems are recurrent in industrial applications and they are in general difficult for...

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Combinatorial Optimization by Decomposition on Hybrid CPU–non-CPU Solver Architectures

By Ali Narimani, Seyed Saeed Rezaei, & Arman Zaribafiyan  The advent of special-purpose hardware such as FPGA- or ASIC-based annealers and quantum processors has shown potential in solving certain families of complex combinatorial optimization problems more efficiently than conventional CPUs. We show that to address an industrial optimization problem, a hybrid architecture of CPUs and...

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