# Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoders

arXiv: https://arxiv.org/abs/2504.13113 · HTML: https://mlsanity.com/paper/2504.13113/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 17 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [Improved Financial Forecasting via Quantum Machine Learning](https://mlsanity.com/paper/2306.12965/) (2023)
- [Unravelling physics beyond the standard model with classical and quantum anomaly detection](https://mlsanity.com/paper/2301.10787/) (2023)
- [Quantum Machine Learning for Finance](https://mlsanity.com/paper/2109.04298/) (2021)
- [A preprocessing perspective for quantum machine learning classification advantage using NISQ algorithms](https://mlsanity.com/paper/2208.13251/) (2022)
- [Quantum Machine Learning for Anomaly Detection in Consumer Electronics](https://mlsanity.com/paper/2409.00294/) (2024)
- [InQMAD: Incremental Quantum Measurement Anomaly Detection](https://mlsanity.com/paper/2210.05061/) (2022)

## Built on by (most cited first, 3 of 3)

- [Three Birds with One Stone: Improving Performance, Convergence, and System Throughput with Nest](https://mlsanity.com/paper/2510.09578/) (2025)
- [Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder](https://mlsanity.com/paper/2606.27411/) (2026)
- [Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments](https://mlsanity.com/paper/2607.20302/) (2026)

## Abstract (arXiv)

Detecting mission-critical anomalous events and data is a crucial challenge across various industries, including finance, healthcare, and energy. Quantum computing has recently emerged as a powerful tool for tackling several machine learning tasks, but training quantum machine learning models remains challenging, particularly due to the difficulty of gradient calculation. The challenge is even greater for anomaly detection, where unsupervised learning methods are essential to ensure practical applicability. To address these issues, we propose Quorum, the first quantum anomaly detection framework designed for unsupervised learning that operates without requiring any training.