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

Effects of Setting the Temperatures in the Parallel Tempering Monte Carlo Algorithm

By Ignacio Rozada, Maliheh Aramon, Jonathan Machta, & Helmut G. Katzgraber

Parallel tempering Monte Carlo has proven to be an efficient method in optimization and sampling applications. Having an optimized temperature set enhances the efficiency of the algorithm through more-frequent replica visits to the temperature limits. The approaches for finding an optimal temperature set can be divided into two main categories. The methods of the first category distribute the replicas such that the swapping ratio between neighbouring replicas is constant and independent of the temperature values…

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Adiabatic Quantum Kitchen Sinks for Learning Kernels Using Randomized Features

By Moslem Noori, Seyed Shakib Vedaie, Inderpreet Singh, Daniel Crawford, Jaspreet S. Oberoi, Barry C. Sanders, & Ehsan Zahedinejad

Quantum information processing is likely to have far-reaching impact in the field of artificial intelligence. While the race to build an error-corrected quantum computer is ongoing, noisy, intermediate-scale quantum (NISQ) devices provide an immediate platform for exploring a possible quantum advantage through hybrid quantum–classical machine learning algorithms. One example of such a hybrid algorithm is “quantum kitchen sinks”, which builds upon the classical algorithm known as “random kitchen sinks” to leverage a gate model quantum computer for machine learning applications…

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A Quantum Annealing-Based Approach to Extreme Clustering

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. We have devised a distributed method that can efficiently solve extreme clustering problems using quantum annealing…

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A Feasibility Pump Algorithm Embedded in an Annealing Framework

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 that embeds the feasibility pump heuristic into an annealing framework. The algorithm dynamically breaks the discrete decision variables into two subsets based on the fractionality information obtained from prior runs, and enforces integrality on each subset separately…

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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…

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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…

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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…

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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 search problem…

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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 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 root of the size of the label space, and in…

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