Mini EXPLORE

Invitation to Share the Future of Quantum ML for Earth Systems Intelligence.

The “Mini Explore” is a peer-led, forward-looking half day workshop designed to connect the quantum, AI, and EO communities in defining the next frontier.

We are embarking on an exciting opportunity with our partners at ESA Φ-lab to explore how Quantum Machine Learning (QML) can enhance AI for Earth Observation (AI4EO) workflows, leveraging the unique advantages of this new computing paradigm.  

Schedule

Date: 25 November 2025  | Time: 13:00 - 16:30 GMT / 14:00 - 17:30 CET 

  • 13:00: Welcome & scene-setting talks

  • 14:00: Big Explore’ briefing + 5 mins break

  • 14:10: Small facilitated discussion groups will tackle specific discussion themes.

  • 15:30: Synthesis & next steps

  • 16:30: Close

Our goal is to understand how QML techniques can

  1. Enhance ML pipelines 

  2. Unlock novel capabilities; and

  3. Better utilise existing data. 

This half-day, interactive session brings together ESA Φ-lab, Trillium Technologies, PennyLane, and leading experts to move beyond theory and co-create actionable directions. A key outcome will be to describe the current state-of-play concretely, identifying what’s genuinely possible today, and where the most promising next steps lie.

The mini explore aims to

  1. Explore the landscape: Shared understanding of QML’s near-term potential for AI4EO.

  2. Identify opportunities: Pinpoint use-cases in climate, disaster response, and agriculture.

  3. Foster collaboration: Build a cross-disciplinary network for ongoing innovation.

  4. Define a path forward: Co-develop a roadmap for research and development.

We’d also like to build a small steering team around QML, and if you’re up for it, we’d love for you to join and help shape the discussions and next steps that emerge from this session.

Briefing : Scoping the Role of Quantum in AI4EO

Quantum computing for machine learning (QML) is in a nascent but rapidly advancing addition to the AI toolbox. While the long-term vision is to leverage quantum's exponential processing power to overcome the limitations of classical AI, the current reality is one of development and exploration. The immediate focus is on developing quantum pipeline enhancements for specific AI tasks like data optimisation, algebraic speed-ups and improved pattern recognition.

Current efforts are directed towards developing quantum machine learning algorithms for tasks such as image analysis, mission planning, and data processing. Early experiments suggest that quantum will be a game changing development to AI for EO (AI4EO) toolbox because of its ability to work with sparse and diverse, high dimensional datasets - a byproduct of the unique qualities of quantum mechanics, however error correction remains an open challenge.

Figure 1: Quantum Machine Learning (QML), shows strong promise in effectively handling sparse data and extracting insights from diverse, high-dimensional inputs, which are notoriously challenging for classical machine learning models.