QArchSearch: A Scalable Quantum Architecture Search Package
This provides an automated approach for quantum computing researchers to explore complex models, though it appears incremental as it builds on existing methods like the QTensor library.
The paper tackles the problem of designing quantum circuits for realizing unitary transformations by introducing QArchSearch, an AI-based quantum architecture search package that scales efficiently to large circuits and has been demonstrated on the Polaris supercomputer.
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.