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While the compilation of quantum algorithms is an inevitable step towards executing programs on quantum processors, the decomposition of the programs into elementary quantum operations poses serious challenge, mostly because in the NISQ era it is advantageous to compress the executed programs as much as possible.
In our recent work [1] we proposed the utilization of FPGA based data-flow engines to partially solve one of these limiting aspects.
By speeding up quantum computer simulations we managed to decompose unitaries up to 9 qubits with an outstanding circuit compression rate.
However, the limit of the available resources on the FPGA chips used in our experiments (Xilinx Alveo U250) prevented us from further scaling up our quantum computer simulator implementation.
In order to circumvent the limiting factor of spatial programming on FPGA chips, in collaboration with Maxeler Technologies (a Groq\textsuperscript{TM} company) we found a novel use of the Groq\textsuperscript{TM} Tensor Streaming Processors (TSPs) which although used broadly for machine learning, they can also provide for a high performance quantum computer simulator. We prove that such data-flow hardware is indeed competitive for these particular problem sizes, being of practical importance and a subject of active research.
[1] Peter Rakyta, Gregory Morse, Jakab Nádori, Zita Majnay-Takács, Oskar Mencer, Zoltán Zimborás, arXiv:2211.07685
