7 papers
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…
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…
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…
Fourier Fingerprints of Ansatzes in Quantum Machine Learning
Melvin Strobl, M. Emre Sahin, Lucas van der Horst +3
Typical schemes to encode classical data in variational quantum machine learning (QML) lead to quantum Fourier models with Fourier basis functions in the num…
Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models
Maja Franz, Melvin Strobl, Leonid Chaichenets +3
Variational quantum algorithms have received substantial theoretical and empirical attention. As the underlying variational quantum circuit (VQC) can be represented by Fourier seri…
QML Essentials -- A framework for working with Quantum Fourier Models
Melvin Strobl, Maja Franz, Eileen Kuehn +2
In this work, we propose a framework in the form of a Python package, specifically designed for the analysis of Quantum Machine Learning models. This framework is based on the Penn…