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Deep neural network approaches for inverse design of superconducting radio-frequency (SRF) cavities and transmon qubits for bosonic quantum computation — mapping target device parameters to candidate geometries.
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| name | neural-inverse-design-srf-cavity |
| description | Deep neural network approaches for inverse design of superconducting radio-frequency (SRF) cavities and transmon qubits for bosonic quantum computation — mapping target device parameters to candidate geometries. |
| category | quantum-computing |
| trigger_words | ["inverse design quantum","SRF cavity","transmon qubit","bosonic quantum computation","neural network device design","electromagnetic optimization","qubit-cavity coupling"] |
Paper: arXiv:2607.02289v1 Authors: Joseph Yaker, Jovan Markovic, Alessandro Reineri, Doga Murat Kurkcuoglu, Silvia Zorzetti
Two deep neural network approaches solve the inverse design problem for SRF cavity-transmon systems: one proposes cavity geometries for target observables, another proposes transmon designs for target qubit-cavity parameters (g, ν_q, α).
