Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators
This provides a tool for non-specialist users and developers in quantum machine learning, but it is incremental as it builds on existing Qiskit primitives.
The authors tackled the problem of integrating quantum computing with traditional machine learning by developing Qiskit Machine Learning, an open-source Python library that provides a high-level API for scalable tasks on quantum hardware and classical simulators, resulting in a modular and intuitive tool available under the Apache 2.0 license.
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.