Neural Inverse Design Srf Cavity

---
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"]
---

# Neural-Network Inverse Design of SRF Cavities for Bosonic Quantum Computation

**Paper**: arXiv:2607.02289v1
**Authors**: Joseph Yaker, Jovan Markovic, Alessandro Reineri, Doga Murat Kurkcuoglu, Silvia Zorzetti

## Core Insight

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).

## Key Results

1. **5% Accuracy**: Recovered cavity designs match targets within ~5%
2. **2% Accuracy**: Transmon designs match targets within ~2%
3. **Fast Alternative**: Maps desired behavior directly to candidate geometries
4. **One-to-Many**: Addresses the inverse-design challenge where multiple geometries can produce same observables

## Two-Level Design Stack

### Level 1: SRF Cavity Geometry
- Input: Target cavity observables
- Output: Candidate cavity geometries
- Verified by end-to-end re-simulation

### Level 2: Transmon Qubit Design
- Input: Target coupling rate (g), qubit frequency (ν_q), anharmonicity (α)
- Output: Transmon geometries + positions within cavity field
- Sensitively depends on both geometry and field position

## Applications

- **Bosonic Quantum Computing**: Long-lived electromagnetic modes for quantum information
- **Device Scaling**: Fast design iteration for growing parameter spaces
- **Quantum Architecture**: Coupling nonlinear elements to cavity modes