Abstract
The authors introduce a search algorithm aimed at optimizing the arrangement of quantum gates, leveraging different sampling techniques to dynamically grow a graph that represents potential quantum circuits. They demonstrate efficacy in reducing the quantum gate count, both theoretically and numerically, albeit with acknowledged computational challenges as the graph expands.
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