[New] Awarded Second Place Prize @ Deloitte’s Quantum Climate Challenge 2024

2nd prize in the Deloitte’s Quantum Climate Challenge 2024 !

Our latest work, “Quantum-Train”, has been revealed on arXiv. In this paper, we introduce an advanced method to reduce the training parameters of “classical” ML models using QML. Specifically, we explore image classification problems. Recently, at the Deloitte Quantum Climate Challenge 2024, we extended this technique to LSTM models for predicting temporal sequential flood data, earning the 2nd prize in the challenge! This general framework can be applied to train basically any classical NN ML model with QML, significantly reducing training parameters. We are excited to extend this approach to various ML tasks that require extensive training parameters!