Qiboml: towards the orchestration of quantum-classical machine learning
- URL: http://arxiv.org/abs/2510.11773v1
- Date: Mon, 13 Oct 2025 18:00:00 GMT
- Title: Qiboml: towards the orchestration of quantum-classical machine learning
- Authors: Matteo Robbiati, Andrea Papaluca, Andrea Pasquale, Edoardo Pedicillo, Renato M. S. Farias, Alejandro Sopena, Mattia Robbiano, Ghaith Alramahi, Simone Bordoni, Alessandro Candido, Niccolò Laurora, Jogi Suda Neto, Yuanzheng Paul Tan, Michele Grossi, Stefano Carrazza,
- Abstract summary: We present Qiboml, an open-source software library for orchestrating quantum and classical machine learning.<n>We showcase its functionalities, including diverse simulation options, noise-aware simulations and real-time error mitigation and calibration.
- Score: 53.28668485072944
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present Qiboml, an open-source software library for orchestrating quantum and classical components in hybrid machine learning workflows. Building on Qibo's quantum computing capabilities and integrating with popular machine learning frameworks such as TensorFlow and PyTorch, Qiboml enables the construction of quantum and hybrid models that can run on a broad range of backends: (i) multi-threaded CPUs, GPUs, and multi-GPU systems for simulation with statevector or tensor network methods; (ii) quantum processing units, both on-premise and through cloud providers. In this paper, we showcase its functionalities, including diverse simulation options, noise-aware simulations, and real-time error mitigation and calibration.
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