AI RESEARCH
QNAS: A Neural Architecture Search Framework for Accurate and Efficient Quantum Neural Networks
arXiv CS.LG
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ArXi:2604.07013v1 Announce Type: cross Designing quantum neural networks (QNNs) that are both accurate and deployable on NISQ hardware is challenging. Handcrafted ansatze must balance expressivity, trainability, and resource use, while limited qubits often necessitate circuit cutting. Existing quantum architecture search methods primarily optimize accuracy while only heuristically controlling quantum and mostly ignore the exponential overhead of circuit cutting. We