Uploaded June 2025 | Updated September 2026, 2 hours ago
In this episode of #LECSTalks we feature Maria Isfan, PhD student at University of Bucharest, who recently gave LISA Early Career Scientists a talk titled "Quantum Neural Networks for LISA Low-Latency Data Analysis Pipelines".
🎤 Tell us something about yourself
I am a PhD student at University of Bucharest and also a research assistant at the Institute of Space Science - a subsidiary of INFLPR in Romania. In my free time, I play guitar.
🎤 What can you tell us about Quantum Neural Networks for LISA Low-Latency Data Analysis Pipelines?
Quantum computing is advancing rapidly, allowing computational speed-ups. As LISA will require fast data processing, we are exploring how quantum neural networks (QNNs) can help gravitational waves (GWs) data analysis. QNNs can learn patterns faster and handle noisy or limited data better than classical methods. In our proof-of-concept studies, we trained QNNs on simulated gravitational-wave signals and achieved accurate signal classification. We also used QNNs to successfully identify most of the GWs signals in the Sangria LISA Data Challenge. These promising results suggest that QNNs could be a powerful tool in LISA’s low-latency data analysis pipeline.
In this episode of #LECSTalks we feature Maria Isfan, PhD student at University of Bucharest, who recently gave LISA Early Career Scientists a talk titled "Quantum Neural Networks for LISA Low-Latency Data Analysis Pipelines".
🎤 Tell us something about yourself
I am a PhD student at University of Bucharest and also a research assistant at the Institute of Space Science - a subsidiary of INFLPR in Romania. In my free time, I play guitar.
🎤 What can you tell us about Quantum Neural Networks for LISA Low-Latency Data Analysis Pipelines?
Quantum computing is advancing rapidly, allowing computational speed-ups. As LISA will require fast data processing, we are exploring how quantum neural networks (QNNs) can help gravitational waves (GWs) data analysis. QNNs can learn patterns faster and handle noisy or limited data better than classical methods. In our proof-of-concept studies, we trained QNNs on simulated gravitational-wave signals and achieved accurate signal classification. We also used QNNs to successfully identify most of the GWs signals in the Sangria LISA Data Challenge. These promising results suggest that QNNs could be a powerful tool in LISA’s low-latency data analysis pipeline.
