Efficient Quantum Simulations using Classical Distribution Techniques

Quantum Simulations aim to help with improving the understanding of quantum effects and test new quantum algorithms and protocols. They serve as a bridge between current state of the art theoretical knowledge and future development of quantum hardware. Advancements in high performance computing, such as new computer architectures, CPU's and GPU's, have pushed the boundaries in classical computing, by scaling devices memory, improving latency of data transfer and efficient data sharing between computers.

Classical quantum simulations have the ability to scale and simulate different quantum environments, in which we can experiment and learn about quantum phenomena.

This work aims to make use of new and emerging (classical) hardware and software packages to further scale quantum classical simulations. As the performance of such simulations is tightly related to the classical machine and techniques used, such as memory, memory bandwidth and others, this opens a great space for research today. Emerging classical hardware is designed to support vast amounts of memory shareable between different servers in a data-center, which is believed to improve quantum classical simulation scalability.

The main questions this research would like to answer is how efficiently quantum simulations can be run on classical devices, how different new and emerging architectures and technologies behave and what are some improvements that can be made to the classical architecture that would benefit this research. The novelty is making use of state of the art technology and report on their performance in this new emerging field of quantum computing.