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- W4387635009 abstract "The current era of quantum computing has yielded several algorithms that promise high computational efficiency. While the algorithms are sound in theory and can provide potentially exponential speedup, there is little guidance on how to design proper quantum circuits to realize the appropriate unitary transformation to be applied to the input quantum state. In this paper, we present texttt{QArchSearch}, an AI based quantum architecture search package with the texttt{QTensor} library as a backend that provides a principled and automated approach to finding the best model given a task and input quantum state. We show that the search package is able to efficiently scale the search to large quantum circuits and enables the exploration of more complex models for different quantum applications. texttt{QArchSearch} runs at scale and high efficiency on high-performance computing systems using a two-level parallelization scheme on both CPUs and GPUs, which has been demonstrated on the Polaris supercomputer." @default.
- W4387635009 created "2023-10-14" @default.
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- W4387635009 date "2023-10-11" @default.
- W4387635009 modified "2023-10-15" @default.
- W4387635009 title "QArchSearch: A Scalable Quantum Architecture Search Package" @default.
- W4387635009 doi "https://doi.org/10.48550/arxiv.2310.07858" @default.
- W4387635009 hasPublicationYear "2023" @default.
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