Quantum SEDONet: Spectrally-Embedded Quantum Deep Operator Networks for Partial Differential Equations
Read the original on arXiv Machine Learning →Quantum SEDONet is a quantum-enhanced deep operator network that embeds spectral features—Fourier for periodic coordinates and Chebyshev for bounded, non‑periodic coordinates—directly into the trunk network. This coordinate‑wise spectral embedding is achieved without adding qubits or circuit depth under unary amplitude encoding, and it reduces mean relative L2 error by up to 54.1% across four PDE benchmarks compared to the baseline Quantum DeepONet. The method demonstrates that quantum and classical inference paths agree to within 10⁻⁸, and it allows simultaneous use of both spectral bases within a single problem, as shown in a mixed‑boundary Poisson channel example.
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