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Deloitte's Quantum Climate Challenge 2024 – Flood Prediction

We invite you to explore quantum computing and machine learning by tackling the challenge to develop a new approach in enhancing flood forecasting and disaster prediction to improve climate resilience.

Highlight for the finalists: Besides an attractive prize pool, the finalists will pitch their solution in front of an expert jury consisting of representatives from the quantum industry.

#quantumcomputing #QuantumML #machinelearning #floodprediction #climatechange

Be part of the Final Pitch Event and register here

  • 🏆  Rewards
    Prize Pool of 12000€ | Access to Quantum Computers and Quantum Simulators
  • 🕑  Deadline
    Apr 28, 2024, 9:59:00 PM
  • 🌎 Scope

    Global

The Challenge
Overview
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Timeline & Rewards
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Quantum Machine Learning (QML) for Flood Prediction

Join Deloitte’s Quantum Climate Challenge 2024!
Deloitte’s Quantum Climate Challenge 2024 aims to explore the potential of quantum computers in enhancing flood forecasting to improve climate resilience.

Climate change has amplified the urgency of disaster prediction in recent years. Rising temperatures and shifting weather patterns have led to more intense floods, wildfires, and other extreme events. As our climate becomes increasingly volatile, accurate forecasting of extreme events can be the difference between life and death.

To advance disaster prediction methods, the challenge seeks to explore the application of Quantum Machine Learning (QML) for forecasting floods along the Wupper River in Germany. Face this challenge and develop a new approach in predicting river floods, leveraging nascent quantum computing technologies. By doing so, you endeavor to assess the prerequisites for quantum hardware to significantly enhance disaster prediction on a larger scale and to gauge the potential timeframe for its implementation.

Goal of the challenge

Due to the limitations of currently accessible quantum hardware, the goal of the challenge is two-fold:

  1. Develop and Train Quantum Models for Next-Day Flood Predictions: The primary focus lies on developing and improving a model that uses quantum computers. Due to limitations of current quantum hardware, we do not expect models to outperform classical models at this stage.
  2. Devise a Path for Handling More Complex Problems: Here, the focus lies on developing a concept for quantum or hybrid methods that may assist the improvement of flood prediction models on further advanced quantum computers. The central objective is to extend lead times for advanced warnings, to enhance the efficacy of disaster preparedness measures.

Detailed challenge description

You can download the detailed challenge description here.

This document provides background information on the relevancy of flood prediction, the Wupper River, and a brief introduction to quantum machine learning.

Resources

Once you have registered for the challenge, the following resources will be available to you via the tab "Confidential Information" that will then be added:

  • IBM Credits to be used for Quantum Hardware and Quantum Simulators
  • AWS credits to be used for Amazon Braket (to be used for simulator and hardware access)
  • Access to D-Wave Quantum Annealers
  • Access to a NVIDIA A100 GPUs
  • Access to the data provided by the Wupperverband
  • Access to Enablement Sessions and Recordings

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