QNAS: A Neural Architecture Search Framework for Accurate and Efficient Quantum Neural Networks
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 introduce QNAS, a neural architecture search framework that unifies hardware aware evaluation, multi objective optimization, and cutting overhead awareness for hybrid quantum classical neural networks (HQNNs). QNAS trains a shared parameter SuperCircuit and uses NSG...
| Authors | Kooshan Maleki, Alberto Marchisio, Muhammad Shafique |
|---|---|
| Primary category | quant-ph |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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