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

• URL: http://arxiv.org/abs/2107.10508v1
• Date: Thu, 22 Jul 2021 08:12:49 GMT
• Title: Multiple Query Optimization using a Hybrid Approach of Classical and Quantum Computing
• Authors: Tobias Fankhauser, Marc E. Sol\`er, Rudolf M. F\"uchslin, Kurt Stockinger
• Abstract summary: We tackle the multiple query optimization problem (MQO) which is an important NP-hard problem in the area of data-intensive problems. We propose a novel hybrid classical-quantum algorithm to solve the MQO on a gate-based quantum computer. Our algorithm shows a qubit efficiency of close to 99% which is almost a factor of 2 higher compared to the state of the art implementation.
• Score: 1.7077661158850292
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum computing promises to solve difficult optimization problems in chemistry, physics and mathematics more efficiently than classical computers, but requires fault-tolerant quantum computers with millions of qubits. To overcome errors introduced by today's quantum computers, hybrid algorithms combining classical and quantum computers are used. In this paper we tackle the multiple query optimization problem (MQO) which is an important NP-hard problem in the area of data-intensive problems. We propose a novel hybrid classical-quantum algorithm to solve the MQO on a gate-based quantum computer. We perform a detailed experimental evaluation of our algorithm and compare its performance against a competing approach that employs a quantum annealer -- another type of quantum computer. Our experimental results demonstrate that our algorithm currently can only handle small problem sizes due to the limited number of qubits available on a gate-based quantum computer compared to a quantum computer based on quantum annealing. However, our algorithm shows a qubit efficiency of close to 99% which is almost a factor of 2 higher compared to the state of the art implementation. Finally, we analyze how our algorithm scales with larger problem sizes and conclude that our approach shows promising results for near-term quantum computers.

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