Selected research papers published by 1QBit’s team and collaborators.
Effective Optimization Using Sample Persistence: A Case Study on Quantum Annealers and Various Monte Carlo Optimization Methods
By Hamed Karimi, Gili Rosenberg, & Helmut G. Katzgraber We present and apply a general-purpose, multi-start algorithm for improving the performance of low-energy samplers used for solving optimization problems. The algorithm iteratively fixes the value of a large portion of the variables to values that have a high probability of being optimal. The resulting problems are smaller and less...
By Sahar Karimi & Pooya Ronagh Recent advancements in quantum annealing hardware and numerous studies in this area suggests that quantum annealers have the potential to be effective in solving unconstrained binary quadratic programming problems. Naturally, one may desire to expand the application domain of these machines to problems with general discrete variables. In this paper, we explore...
By Anna Levit, Daniel Crawford, Navid Ghadermarzy, Jaspreet S. Oberoi, Ehsan Zahedinejad, & Pooya Ronagh Recent theoretical and experimental results suggest the possibility of using current and near-future quantum hardware in challenging sampling tasks. In this paper, we introduce free-energy-based reinforcement learning (FERL) as an application of quantum hardware. We propose a method for...
By Daniel Crawford, Anna Levit, Navid Ghadermarzy, Jaspreet S. Oberoi, & Pooya Ronagh
Posted on December 17, 2016
By Sahar Karimi & Pooya Ronagh
Posted on January 25, 2017
By Maritza Hernandez & Maliheh Aramon
Quantum annealing is a promising technique which leverages quantum mechanics to solve hard optimization problems. Considerable progress has been made in the development of a physical quantum annealer, motivating the study of methods to enhance the efficiency of such a solver. In this work, we present a quantum annealing approach to measure similarity among molecular structures. Implementing real-world problems on a quantum annealer is challenging due to hardware limitations such as sparse connectivity, intrinsic control error, and limited precision. In order to overcome the limited connectivity, a problem must be reformulated using minor-embedding techniques. Using a real data set, we investigate the performance of a quantum annealer in solving the molecular similarity problem. We provide experimental evidence that common practices for embedding can be replaced by new alternatives which mitigate some of the hardware limitations and enhance its performance. Common practices for embedding include minimizing either the number of qubits or the chain length, and determining the strength of ferromagnetic couplers empirically. We show that current criteria for selecting an embedding do not improve the hardware’s performance for the molecular similarity problem. Furthermore, we use a theoretical approach to determine the strength of ferromagnetic couplers. Such an approach removes the computational burden of the current empirical approaches, and also results in hardware solutions that can benefit from simple local classical improvement. Although our results are limited to the problems considered here, they can be generalized to guide future benchmarking studies.
By Hamed Karimi & Gili Rosenberg
Posted on June 24, 2016
By Raouf Dridi & Hedayat Alghassi
Posted on April 20, 2016
By Arman Zaribafiyan, Dominic Marchand, & S. Saeed C. Rezaei
Posted on February 15, 2016
By Maritza Hernandez, Arman Zaribafiyan, Maliheh Aramon, & Mohammad Naghibi
Posted on January 26, 2016