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

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…

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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 classical computing hardware…

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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 non-CPU devices is inevitable…

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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 the possibility of employing quantum annealers to solve unconstrained quadratic programming problems over a bounded integer domain…

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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…

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Reinforcement Learning Using Quantum Boltzmann Machines

By Daniel Crawford, Anna Levit, Navid Ghadermarzy, Jaspreet S. Oberoi, & Pooya Ronagh

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…

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