Comparing Classical and Quantum Deep Learning Techniques for Anomaly Detection of Short-Duration Gamma-Ray Signals

Gamma-Ray Bursts (GRBs) are the most luminous explosions in the universe, and are of great interest in astrophysics research. Despite the development of onboard trigger algorithms, significant obstacles remain in identifying weak events and effectively filtering out false detections. These challenges motivate the exploration of innovative technologies such as deep learning and quantum computing to help traditional triggers as a second verification level and increase reliability across the detection pipeline. We present a comparison of a classical and a quantum autoencoder running on simulated quantum hardware, tackling an anomaly detection task in a controlled environment. The goal is to compare their performance, with a focus on resource-constrained scenarios involving limited data and trainable parameters. Both approaches were tested using a simulated set of light curves from the anticoincidence shield of the Compton Spectrometer and Imager (COSI) soft gamma-ray telescope. Short-duration GRB signals were simulated using MEGAlib and then modified through data augmentation techniques. The results indicate that while classical autoencoders reconstruct better, achieving a Mean Squared Error (MSE) as low as 2 . 97 × 1 0 − 5 , the efficiency of the quantum autoencoders is notable with lower numbers of parameters and smaller datasets. Using only 10 trainable parameters and 100 input samples, the quantum model obtained an MSE of 2 . 24 × 1 0 − 4 , outperforming its classical counterpart (MSE of 3 . 81 × 1 0 − 2 with 705 parameters). These results show a promising role for quantum machine learning in astrophysical contexts where data related to GRBs are limited or lightweight model architectures are essential.