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

arXiv: https://arxiv.org/abs/2505.17756 · HTML: https://mlsanity.com/paper/2505.17756/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 23 May 2025.

## Builds on (most cited first, 13 of 13)

- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [A rigorous and robust quantum speed-up in supervised machine learning](https://mlsanity.com/paper/2010.02174/) (2020)
- [Quantum Inference on Bayesian Networks](https://mlsanity.com/paper/1402.7359/) (2014)
- [Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage](https://mlsanity.com/paper/2204.04017/) (2022)
- [Quantum Kernel Alignment with Stochastic Gradient Descent](https://mlsanity.com/paper/2304.09899/) (2023)
- [When Machine Learning Meets Quantum Computers: A Case Study](https://mlsanity.com/paper/2012.10360/) (2020)
- [Quantum circuit representation of Bayesian networks](https://mlsanity.com/paper/2004.14803/) (2020)
- [Efficient Parameter Optimisation for Quantum Kernel Alignment: A Sub-sampling Approach in Variational Training](https://mlsanity.com/paper/2401.02879/) (2024)
- [Symmetry breaking in geometric quantum machine learning in the presence of noise](https://mlsanity.com/paper/2401.10293/) (2024)
- [Quantum Convolutional Neural Networks for the detection of Gamma-Ray Bursts in the AGILE space mission data](https://mlsanity.com/paper/2404.14133/) (2024)
- [Quantum Text Classifier -- A Synchronistic Approach Towards Classical and Quantum Machine Learning](https://mlsanity.com/paper/2305.12783/) (2023)
- [Benchmarking Quantum Convolutional Neural Networks for Signal Classification in Simulated Gamma-Ray Burst Detection](https://mlsanity.com/paper/2501.17041/) (2025)

## Built on by (most cited first, 7 of 7)

- [Comparative Analysis of QNN Architectures for Wind Power Prediction: Feature Maps and Ansatz Configurations](https://mlsanity.com/paper/2506.14795/) (2025)
- [From Classical to Hybrid: A Practical Framework for Quantum-Enhanced Learning](https://mlsanity.com/paper/2511.08205/) (2025)
- [Modeling Quantum Autoencoder Trainable Kernel for IoT Anomaly Detection](https://mlsanity.com/paper/2511.21932/) (2025)
- [Efficient Mutation Testing of Quantum Machine Learning Models](https://mlsanity.com/paper/2605.00107/) (2026)
- [Quantum-classical hybrid models based on error correction for time series forecasting](https://mlsanity.com/paper/2606.15213/) (2026)
- [Towards quantum machine learning for assessing the resilience of post-quantum cryptography](https://mlsanity.com/paper/2607.13722/) (2026)
- [Impact of Data Loss in Postprocessing on Training and Inference of Quantum Neural Networks](https://mlsanity.com/paper/2609.05060/) (2026)

## Abstract (arXiv)

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.