2025ApJ...987..105K

We investigate whether a novel method of quantum machine learning 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 long short-term memory (LSTM) where some fully connected layers are replaced with quantum circuits. LSTM, making predictions based on preceding data, allows for the 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 these events 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 flare-like quasiperiodic eruptions to the training data. Comparing various aspects of the performances of the quantum and classical LSTM (CLSTM) models, we find that QLSTM models incorporating quantum superposition and entanglement slightly outperform the 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 40,154 light curves in the 0.2-12 keV band, we detect 113 light curves with anomalies, or transient event candidates. This number is ≈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 the Wide-field Infrared Survey Explorer, no possible counterparts are found for 12 detected anomalies.