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

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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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...

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Practical Integer-to-Binary Mapping for Quantum Annealers

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...

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Free-Energy-based Reinforcement Learning Using a Quantum Processor

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...

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Reinforcement Learning Using Quantum Boltzmann Machines

By Daniel Crawford, Anna Levit, Navid Ghadermarzy, Jaspreet S. Oberoi, & Pooya Ronagh
Posted on December 17, 2016

We investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks. We associate a transverse fi eld Ising spin Hamiltonian with a layout of qubits similar to that of a deep Boltzmann machine (DBM) and use simulated quantum annealing (SQA) to numerically simulate quantum sampling from this system. We design a reinforcement learning algorithm in which the set of visible nodes representing the states and actions of an optimal policy are the fi rst and last layers of the deep network. In absence of a transverse eld, our simulations show that DBMs train more e ffectively than restricted Boltzmann machines (RBM) with the same number of weights. Since sampling from Boltzmann distributions of a DBM is not classically feasible, this is evidence of advantage of a non-Turing sampling oracle. We then develop a framework for training the network as a quantum Boltzmann machine (QBM) in the presence of a signifi cant transverse field for reinforcement learning. This further improves the reinforcement learning method using DBMs.

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