Quantum-Inspired Robust and Scalable SAR Object Classification

SAR image classification naturally has to deal with huge noise and a high dynamic range particularly requiring robust classification models. Additionally, the deployment of these models on edge devices, such as drones and military aircraft, requires a careful balance between model size and classification accuracy. This study explores the potential of tensor networks to meet these robustness requirements, specifically evaluating their resilience to data poisoning. Unlike previous works that concentrated on conventional neural networks for SAR object detection, this research focuses on the robustness and model reduction capabilities of tensor networks in object classification. Our findings indicate that tensor networks are adept at addressing both the challenges of robustness and the need for model efficiency, thereby contributing valuable insights to the ongoing discourse in radar applications and deep learning methodologies in general.

Quantum-inspired Machine Learning on high-energy physics data

One of the most challenging big data problems in high energy physics is the analysis and classification of the data produced by the Large Hadron Collider at CERN. Recently, machine learning techniques have been employed to tackle such challenges, which, despite being very effective, rely on classification schemes that are hard to interpret. Here, we introduce and apply a quantum-inspired machine learning technique and, exploiting tree tensor networks, we show how to efficiently classify b-jet events in proton-proton collisions at LHCb and to interpret the classification results. In particular, we show how to select important features and adapt the network geometry based on information acquired in the learning process. Moreover, the tree tensor network can be adapted for optimal precision or fast response in time without the need for repeating the learning process. This paves the way to high-frequency real-time applications as needed for current and future LHC event classification to trigger events at the tens of MHz scale.