Software Between Quantum and Machine Learning -- And Down to Pulses

• URL: http://arxiv.org/abs/2605.21286v1
• Date: Wed, 20 May 2026 15:20:07 GMT
• Title: Software Between Quantum and Machine Learning -- And Down to Pulses
• Abstract summary: We present a software framework that extends quantum machine learning (QML) methodologies to encompass pulse-level modelling.<n>The framework provides a comprehensive suite of modelling and analytical capabilities.<n>All performance-critical components are implemented in a high-performance environment using JAX and supported by a dedicated quantum simulator.
• Score: 0.9569512179866463
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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