most citedQML Essentials -- A framework for working with Quantum Fourier Models

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quant-ph2026

JAQSI: Just Another Quantum Simulator

Melvin Strobl, Maja Franz, Lukas Scheller +3

Classical simulation supports the development and validation of quantum algorithms through full-state inspection, controlled noise models and backpropagation, albeit for small quan…

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

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…

quant-ph2025

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…

quant-ph2025★ 1 cited

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…

quant-ph2025

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…

quant-ph2025

From Hope to Heuristic: Realistic Runtime Estimates for Quantum Optimisation in NHEP

Maja Franz, Manuel Schönberger, Melvin Strobl +5

Noisy Intermediate-Scale Quantum (NISQ) computers, despite their limitations, present opportunities for near-term quantum advantages in Nuclear and High-Energy Physics (NHEP) when…