Software Between Quantum and Machine Learning -- And Down to Pulses
Abstract
Contemporary quantum computing platforms remain, in essence, programmable physical systems whose control is typically mediated through unitary gate abstractions. While such abstractions provide a uniform interface, they obscure important aspects of the underlying hardware and may limit the exploitation of its full capabilities. Direct operation at the control-pulse level offers a more expressive and physically faithful paradigm, enabling, for instance, the implementation of tailored error-mitigation and optimisation strategies. However, this increased expressivity comes at the cost of greater quantum software development complexity, necessitating structured and accessible tooling. We present a software framework, integrated within the QML-Essentials package, that extends quantum machine learning (QML) methodologies to encompass pulse-level modelling. By embedding quantum optimal control techniques within a QML setting, our approach enables the seamless combination of gate-based and pulse-level representations. The framework provides a comprehensive suite of modelling and analytical capabilities. In particular, we introduce composable ansatz constructions based on interchangeable building blocks, and support for end-to-end optimisation of pulse parameters. Motivated by the central role of quantum Fourier models, we further incorporate a range of Fourier-analytic diagnostics, complemented by extended measures of entanglement. All performance-critical components are implemented in a high-performance environment using JAX and supported by a dedicated quantum simulator. Taken together, the framework facilitates reproducible and systematic investigations, while bridging the conceptual and practical divide between abstract circuit models and hardware-aware optimisation. It provides a robust foundation for future developments at the intersection of QML and quantum control.
Versions
Related
- Benchmarking Dark Matter Search using a Parity-Check Protocol with Machine-Learning Optimized Pulses
- Robust high-order quantum simulation using finite-width pulses
- When Quantum Meets AI: Quantum Methods for Machine Learning and Machine Learning Methods for Quantum Systems
- Application a quantum machine learning algorithm to the applied problem of modeling the thermal regime of a servers
- Photonic Time-Delayed Quantum Extreme Learning Machine
BibTeX
@misc{franz2026software,
title = {{Software Between Quantum and Machine Learning -- And Down to Pulses}},
author = {Maja Franz and Melvin Strobl and Jonathan Hunz and Lukas Scheller and Lucas van der Horst and Eileen Kuehn and Achim Streit and Wolfgang Mauerer},
howpublished = {arXiv},
year = {2026},
month = jun,
doi = {10.48550/arxiv.2605.21286},
eprint = {2605.21286},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.21286},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Software Between Quantum and Machine Learning -- And Down to Pulses
AU - Maja Franz
AU - Melvin Strobl
AU - Jonathan Hunz
AU - Lukas Scheller
AU - Lucas van der Horst
AU - Eileen Kuehn
AU - Achim Streit
AU - Wolfgang Mauerer
PY - 2026
DA - 2026/06/01
PB - arXiv
DO - 10.48550/arxiv.2605.21286
UR - https://arxiv.org/abs/2605.21286
AB - Contemporary quantum computing platforms remain, in essence, programmable physical systems whose control is typically mediated through unitary gate abstractions. While such abstractions provide a uniform interface, they obscure important aspects of the underlying hardware and may limit the exploitation of its full capabilities. Direct operation at the control-pulse level offers a more expressive and physically faithful paradigm, enabling, for instance, the implementation of tailored error-mitigation and optimisation strategies. However, this increased expressivity comes at the cost of greater quantum software development complexity, necessitating structured and accessible tooling. We present a software framework, integrated within the QML-Essentials package, that extends quantum machine learning (QML) methodologies to encompass pulse-level modelling. By embedding quantum optimal control techniques within a QML setting, our approach enables the seamless combination of gate-based and pulse-level representations. The framework provides a comprehensive suite of modelling and analytical capabilities. In particular, we introduce composable ansatz constructions based on interchangeable building blocks, and support for end-to-end optimisation of pulse parameters. Motivated by the central role of quantum Fourier models, we further incorporate a range of Fourier-analytic diagnostics, complemented by extended measures of entanglement. All performance-critical components are implemented in a high-performance environment using JAX and supported by a dedicated quantum simulator. Taken together, the framework facilitates reproducible and systematic investigations, while bridging the conceptual and practical divide between abstract circuit models and hardware-aware optimisation. It provides a robust foundation for future developments at the intersection of QML and quantum control.
ER -