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Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators

Year: 2025

Type: preprint

Abstract: We present Qiskit Machine Learning (ML), a high-level Python library that combines elements of quantum computing with traditional machine learning. The API abstracts Qiskit's primitives to facilitate interactions with classical simulators and quantum hardware. Qiskit ML started as a proof-of-concept code in 2019 and has since been developed to be a modular, intuitive tool for non-specialist users while allowing extensibility and fine-tuning controls for quantum computational scientists and developers. The library is available as a public, open-source tool and is distributed under the Apache version 2.0 license.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2505.17756

Authors: Sahin, M. Emre, Altamura, Edoardo, Wallis, Oscar, Wood, Stephen P., Dekusar, Anton, Millar, Declan A., Imamichi, Takashi, Matsuo, Atsushi, Mensa, Stefano

Institutions: Daresbury Laboratory, Sci-Tech Daresbury, IBM (United States), IBM Research - Ireland, IBM (United Kingdom), IBM Research - Tokyo

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: International Business Machines Corporation, Science and Technology Facilities Council

OpenAlex ID: W6910595809

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Cited by: 3

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