Federated Learning with Quantum Enhanced LSTM for Applications in High Energy Physics

2026-04-17

AI Summary

This work addresses the challenge of achieving high accuracy, strong privacy guarantees, and computational efficiency in data-intensive and privacy-sensitive domains such as high-energy physics. The authors propose a lightweight hybrid architecture—Quantum-enhanced LSTM (QLSTM)—that integrates federated learning with a variational quantum circuit-augmented LSTM. With fewer than 300 trainable parameters, QLSTM matches the performance of classical deep learning models using only 20,000 training samples, reducing both data and computational overhead by two orders of magnitude. By leveraging the expressive power of variational quantum circuits for modeling complex feature interactions and the temporal modeling strength of LSTM, the method outperforms existing variational quantum classifier (VQC) approaches on the 5-million-sample SUSY dataset, maintaining classification accuracy within ±1% of classical benchmarks.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Security and privacy of machine learning and AI applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Learning with large-scale datasets and information-critical applications, such as in High Energy Physics (HEP), demands highly complex, large-scale models that are both robust and accurate. To tackle this issue and cater to the learning requirements, we envision using a federated learning framework with a quantum-enhanced model. Specifically, we design a hybrid quantum-classical long-shot-term-memory model (QLSTM) for local training at distributed nodes. It combines the representative power of quantum models in understanding complex relationships within the feature space, and an LSTM-based model to learn necessary correlations across data points. Given the computing limitations and unprecedented cost of current stand-alone noisy-intermediate quantum (NISQ) devices, we propose to use a federated learning setup, where the learning load can be distributed to local servers as per design and data availability. We demonstrate the benefits of such a design on a classification task for the Supersymmetry(SUSY) dataset, having 5M rows. Our experiments indicate that the performance of this design is not only better that some of the existing work using variational quantum circuit (VQC) based quantum machine learning (QML) techniques, but is also comparable ($Δ\sim \pm 1\%$) to that of classical deep-learning benchmarks. An important observation from this study is that the designed framework has $<$300 parameters and only needs 20K data points to give a comparable performance. Which also turns out to be a 100$\times$ improvement than the compared baseline models. This shows an improved learning capability of the proposed framework with minimal data and resource requirements, due to the joint model with an LSTM based architecture and a quantum enhanced VQC.

Problem

• Federated Learning
• Quantum Machine Learning
• High Energy Physics
• NISQ Devices
• Large-scale Models

Innovation

• Federated Learning
• Quantum-enhanced LSTM
• Hybrid Quantum-Classical Model
• NISQ
• High Energy Physics