Deep neural networks accelerate quantum computing hardware design

Researchers have developed two deep neural network (DNN) approaches to tackle the inverse design problem for superconducting radio-frequency (SRF) cavities and transmon qubits used in bosonic quantum computation. These DNNs can rapidly generate candidate device geometries that meet specific electromagnetic and coupling targets, significantly reducing the computational cost compared to traditional iterative simulation methods. The first DNN proposes SRF cavity designs, while the second designs transmon qubits to achieve desired coupling rates, qubit frequencies, and anharmonicities, with recovered designs matching targets within a few percent. AI

IMPACT Accelerates the design process for quantum computing hardware by leveraging deep learning.

RANK_REASON The cluster contains an academic paper detailing a new research methodology. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Bosonic Quantum Computation
• DagsHub
• deep neural network
• Hugging Face
• Schweizer Radio und Fernsehen
• Transmon

• paper
• infra

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