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Quantum Machine Learning Quantum Simulation

Qiboml: towards the orchestration of quantum-classical machine learning

arXiv

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

Year

2025

Paper ID

51278

Status

Preprint

Abstract Read

~2 min

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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.

Why This Paper Matters

This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
It adds a 2025 reference point for readers tracking recent quantum research.
We present Qiboml, an open-source software library for orchestrating quantum and classical components in hybrid machine learning workflows.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2510.11773 [2] arXiv https://arxiv.org/abs/2510.11773 [3] Publisher https://arxiv.org/abs/2510.11773

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