Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves

TL;DR

This paper explores a quantum machine learning approach using a quantum LSTM to identify transient events in X-ray light curves, demonstrating slight performance improvements over classical models and discovering new anomaly candidates.

Contribution

It introduces a quantum LSTM architecture for time-series anomaly detection in astrophysics, showing its advantages over classical LSTM models in accuracy and true-positive rate.

Findings

QLSTM slightly outperforms CLSTM in accuracy and true-positive rate.

113 transient event candidates identified, 12 with no counterparts.

Quantum superposition and entanglement enhance model expressiveness.

Abstract

We investigate whether a novel method of quantum machine learning (QML) can identify anomalous events in X-ray light curves as transient events and apply it to detect such events from the XMM-Newton 4XMM-DR14 catalog. The architecture we adopt is a quantum version of the long-short term memory (LSTM) where some fully connected layers are replaced with quantum circuits. The LSTM, making predictions based on preceding data, allows identification of anomalies by comparing predicted and actual time-series data. The necessary training data are generated by simulating active galactic nucleus-like light curves as the species would be a significant population in the XMM-Newton catalog. Additional anomaly data used to assess trained quantum LSTM (QLSTM) models are produced by adding flares like quasi-periodic eruptions to the training data. Comparing various aspects of the performances of the quantum and classical LSTM models, we find that QLSTM models incorporating quantum superposition and entanglement slightly outperform the classical LSTM (CLSTM) model in expressive power, accuracy, and true-positive rate. The highest-performance QLSTM model is then used to identify transient events in 4XMM-DR14. Out of 40154 light curves in the 0.2--12 keV band, we detect 113 light curves with anomalies, or transient event candidates. This number is $\approx$ 1.3 times that of anomalies detectable with the CLSTM model. By utilizing SIMBAD and four wide-field survey catalogs made by ROSAT, SkyMapper, Pan-STARRS, and WISE, no possible counterparts are found for 12 detected anomalies.