arXiv Machine Learning
Neural Modal Decomposition: Architectural Priors from Observables
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arXiv AI
Jul 7
Neural-Network Inverse Design of SRF Cavities and Transmons for Bosonic Quantum Computation
arXiv:2607. 02289v1 Announce Type: cross Abstract: Three-dimensional superconducting radio-frequency (SRF) cavities provide exceptionally long-lived electromagnetic modes and, when coupled to nonlinear elements such as transmon qubits, become promising architectures for bosonic quantum information processing.
arXiv Machine Learning
Jul 27
Quantum Spectral Model: Data Reuploading with Input-Conditioned Frequency Support
arXiv:2607. 22516v1 Announce Type: cross Abstract: A central design principle in modern machine learning and artificial intelligence is to align a model's inductive bias with the structure of its input data.
arXiv Machine Learning
Jul 23
Q-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices
arXiv:2605. 22097v2 Announce Type: replace-cross Abstract: Photonic quantum computing is a promising platform for scalable quantum machine learning, but designing effective hybrid architectures remains challenging under hardware and optimization constraints.
arXiv Machine Learning
Aug 27
When Does Frequency Decomposition Benefit Physics-Informed Neural Networks? A Preliminary Ablation Study
The paper investigates when frequency decomposition aids Physics-Informed Neural Networks (PINNs) by introducing a dual‑branch, spectrally‑gated architecture (DBSG‑PINN) that separates low‑ and high‑frequency components. Experiments on five one‑dimensional PDE benchmarks show that frequency decomposition significantly reduces error—up to 59.2% on a multimodal wave problem—when the target solution is spectrally complex, but offers little improvement on smoother problems and can even worsen performance on a simple 1D wave benchmark. The adaptive gate’s effectiveness scales with the spectral richness of the solution, suggesting it exploits frequency structure rather than adding noise.
arXiv Machine Learning
Aug 11
Hybrid Quantum-Classical PINNs for Scientific Computing: A Multi-GPU Open-Source Framework
arXiv:2604. 15645v2 Announce Type: replace Abstract: We present QPINNACLE, an open-source computational framework for physics-informed neural networks (PINNs) that integrates modern training strategies, multi-GPU acceleration, and hybrid quantum-classical architectures within a unified modular workflow.
arXiv AI
Jul 29
DDSNet: Dual-domain Symmetry-aware Network for PCSEL Property Prediction
arXiv:2607. 24785v1 Announce Type: cross Abstract: Efficient exploration of the photonic crystal (PhC) lattice design space is essential for developing photonic crystal surface-emitting lasers.