European researchers petition for funding to integrate machine learning and quantum science

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Insider Brief

  • European scientists and quantum supporters have launched a petition calling for increased funding for research at the interface of machine learning and quantum science.
  • Machine learning is expected to play a crucial role in improving quantum computing hardware and software, facilitating the discovery of new molecules and materials, and automating complex quantum experiments.
  • Proponents suggest that significant investment is needed to unlock the combined power of the fields to drive significant scientific and practical advances.

A group of leading European researchers have launched a petition calling for increased funding at the interface of machine learning and quantum science, aiming to position Europe as a leader in next-generation quantum technologies. The petition highlights the transformative potential of integrating machine learning with quantum science, highlighting the need for significant investment to unlock synergies that could drive significant advances in both fields.

The Potential of Machine Learning in Quantum Science

The petition describes the considerable promise that machine learning holds for quantum science, largely due to the data-intensive nature of quantum research and the advanced computational capabilities of modern machine learning tools. By combining these disciplines, researchers aim to increase the efficiency and effectiveness of quantum experiments, potentially leading to breakthroughs in quantum technologies and fundamental physics.

According to the petition, machine learning is expected to play a crucial role in improving quantum computing hardware and software, facilitating the discovery of new molecules and materials and automating complex quantum experiments. Furthermore, machine learning algorithms can optimize quantum systems, develop new quantum algorithms, and enable precise manipulation of quantum devices, paving the way for energy-efficient control protocols and other practical benefits.

Expected progress

The integration of machine learning into quantum science is predicted to bring numerous advances in various fields. The researchers highlight several key areas where significant progress is expected:

  1. Quantum Computing Improvements: Machine learning can improve both hardware and software components of quantum computing, making these systems more efficient and accessible.
  2. Molecular and material discoveries: Combining machine learning algorithms with quantum simulations can accelerate the discovery of new molecules and materials with unique properties.
  3. Optimized quantum experiments: Automating and optimizing quantum experiments through machine learning techniques can lead to faster and more accurate results.
  4. New quantum algorithms: Developing innovative quantum algorithms with machine learning can solve complex problems currently beyond the reach of classical computing.
  5. Energy Efficient Control Protocols: Machine learning can help design energy-efficient protocols for controlling quantum systems, increasing their practicality and scalability.

Call for Investment and Cooperation

To realize these advances, the petition emphasizes the need for significant investment in both basic and applied research. It calls for fostering interdisciplinary collaboration between quantum physicists, machine learning engineers, computer scientists and industry stakeholders. The researchers argue that creating a strong ecosystem of open source software, standardized datasets, and community-driven projects will facilitate progress and innovation.

The petition also highlights the importance of training the next generation of researchers through specialized programs and increasing public engagement through effective science communication. By building a strong foundation for interdisciplinary research and development, Europe can maintain its competitive edge and lead in the rapidly developing field of quantum technologies.

For more information and to view the full manifesto, visit the petition’s official website. The petition itself is available on the Open Petition page here.