3 papers
quant-ph2026
Software Between Quantum and Machine Learning -- And Down to Pulses
Maja Franz, Melvin Strobl, Jonathan Hunz +5
Contemporary quantum computing platforms remain, in essence, programmable physical systems whose control is typically mediated through unitary gate abstractions. While such abstrac…
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…
quant-ph2026
Beyond Gates: Pulse Level Quantum Fourier Models
Melvin Strobl, Maja Franz, Lukas Scheller +3
In the domain of variational quantum algorithms, quantum Fourier models (QFMs) provide a mathematically well defined structure for quantum machine learning (QML). There has been a…