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

TL;DR

Qiskit Machine Learning is an open-source Python library that integrates quantum computing with classical machine learning, enabling scalable quantum ML tasks on simulators and hardware with user-friendly APIs.

Contribution

It introduces a modular, extensible library that simplifies quantum machine learning for both non-specialists and experts, bridging quantum computing and ML workflows.

Findings

Provides a high-level API for quantum ML tasks

Supports interaction with quantum hardware and simulators

Open-source and extensible for future developments

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.