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quant-ph2026
Unflattening by Flattening -- How Input Distributions Shape Output Variance in Angle-Encoded Circuits
Melvin Strobl, Gabriel Mejia, Eileen Kuehn +1
Barren plateaus hinder training of parameterized quantum circuits by making loss gradients exponentially small in the number of qubits. For angle-encoded product states, we show ho…
quant-ph2026
Trainable Quantum Spectral Models for Partial Differential Equations
Gabriel Mejia, Eileen Kuehn, Melvin Strobl +1
This work studies trainable quantum spectral models (QSMs) for solving linear partial differential equations (PDEs). Instead of learning solutions directly in physical space, QSMs…