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WORLD SCIENTIFIC eBooks

Quantum Problem-Solving

Published Jul 15, 2020• 0 citations
Publisher
DOI
10.1142/9789811224317_0011

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Quantum computing has shown great potential and advantages in solving integer factorization and disordered database search. However, it is not easy to solve specific problems with quantum computing device efficiently and widely, because a lot of professional background knowledge is required. In order to solve this problem, we propose an optimization problem’s automatic hybird quantum framework (OpAQ) for solving user-specified problems on a hybrid computing architecture including both quantum and classical computing resources. Such a solver can allow nonprofessionals who are not familiar with quantum physics and quantum computing to use quantum computing device to solve some classically difficult problems easily. Combinatorial optimization problem is one of the most important problems in both academic and industry. In this paper, we mainly focus on these problems and solve them with OpAQ, which is based on quantum approximation optimization algorithm (QAOA). We evaluate the performance of our approach in solving Graph Coloring, Max-cut, Traveling Salesman and Knapsack Problem. The experimental results show that quantum solver can achieve almost the same optimal solutions with the classical.
Some problems are computationally solvable but to complexity of nature of problem, they take a long time to solve. Quantum computers (QC) offer the ability to solve these problems in reasonable time using principles of quantum mechanics. One such hard problem is MaxCut, with various applications like portfolio optimization, drug discovery, and inventory management. This poster dives into solving these problems on QC with introduction to how problems are encoded mathematically (QUBO); converted into quantum language (Hamiltonian) and solved using quantum algorithm (QAOA).The goal of research was to run MaxCut problem on different variants of a quantum algorithms to analyze results.