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Quantum computing promises to solve difficult optimization problems inchemistry, physics and mathematics more efficiently than classical computers,but requires fault-tolerant quantum computers with millions of qubits. Toovercome errors introduced by today's quantum computers, hybrid algorithmscombining classical and quantum computers are used. In this paper we tackle themultiple query optimization problem (MQO) which is an important NP-hard problemin the area of data-intensive problems. We propose a novel hybridclassical-quantum algorithm to solve the MQO on a gate-based quantum computer.We perform a detailed experimental evaluation of our algorithm and compare itsperformance against a competing approach that employs a quantum annealer --another type of quantum computer. Our experimental results demonstrate that ouralgorithm currently can only handle small problem sizes due to the limitednumber of qubits available on a gate-based quantum computer compared to aquantum computer based on quantum annealing. However, our algorithm shows aqubit efficiency of close to 99% which is almost a factor of 2 higher comparedto the state of the art implementation. Finally, we analyze how our algorithmscales with larger problem sizes and conclude that our approach shows promisingresults for near-term quantum computers.

Multiple Query Optimization using a Hybrid Approach of Classical and Quantum Computing