Simulation of Quantum Computers: Review and Acceleration Opportunities

• URL: http://arxiv.org/abs/2410.12660v1
• Date: Wed, 16 Oct 2024 15:19:12 GMT
• Title: Simulation of Quantum Computers: Review and Acceleration Opportunities
• Authors: Alessio Cicero, Mohammad Ali Maleki, Muhammad Waqar Azhar, Anton Frisk Kockum, Pedro Trancoso,
• Abstract summary: Quantum computing has the potential to revolutionize multiple fields by solving complex problems that can not be solved in reasonable time with current classical computers. Currently, the most practical way to develop and test quantum algorithms is to use classical simulators of quantum computers. This review provides an overview of the components of a quantum computer, the levels at which these components and the whole quantum computer can be simulated, and an analysis of different state-of-the-art acceleration approaches.
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• Abstract: Quantum computing has the potential to revolutionize multiple fields by solving complex problems that can not be solved in reasonable time with current classical computers. Nevertheless, the development of quantum computers is still in its early stages and the available systems have still very limited resources. As such, currently, the most practical way to develop and test quantum algorithms is to use classical simulators of quantum computers. In addition, the development of new quantum computers and their components also depends on simulations. Given the characteristics of a quantum computer, their simulation is a very demanding application in terms of both computation and memory. As such, simulations do not scale well in current classical systems. Thus different optimization and approximation techniques need to be applied at different levels. This review provides an overview of the components of a quantum computer, the levels at which these components and the whole quantum computer can be simulated, and an in-depth analysis of different state-of-the-art acceleration approaches. Besides the optimizations that can be performed at the algorithmic level, this review presents the most promising hardware-aware optimizations and future directions that can be explored for improving the performance and scalability of the simulations.

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