Executive Summary This proposal outlines an innovative approach to accelerating nuclear fusion development by combining Google DeepMind's GNoME AI system with the MQ quantum chip. This integration aims to overcome key challenges in fusion research through advanced materials discovery and plasma simulations, operating under a public-benefit model to ensure global impact.

The Energy Challenge and Fusion Solution The world faces an urgent need to transition from fossil fuels, which drive climate change and create geopolitical instability. While renewable energy sources offer partial solutions, they face limitations in energy density and scalability. Nuclear fusion emerges as a promising alternative, offering the potential for nearly limitless clean energy through the same process that powers the sun. However, achieving practical fusion requires overcoming significant technical hurdles, particularly in developing materials that can withstand extreme conditions and maintaining stable plasma states.

Technological Integration GNoME, developed by Google DeepMind, revolutionizes materials discovery by combining deep neural networks with evolutionary algorithms. It excels at predicting material properties and identifying promising candidates for fusion reactor components, even with limited experimental data. The MQ chip complements this capability by providing quantum-enhanced simulations of atomic-scale interactions and plasma physics. This hybrid quantum-classical platform enables unprecedented accuracy in modeling fusion processes.

The integration of these technologies creates a powerful workflow: GNoME identifies promising materials, while the MQ chip conducts detailed quantum simulations to validate and refine these predictions. This iterative process, informed by real-world testing, dramatically accelerates the development cycle. The system learns from each iteration, continuously improving its predictive capabilities and simulation accuracy.

Global Impact and Implementation The successful implementation of this integrated approach could transform global energy production. By providing clean, sustainable energy at scale, it would help address climate change while promoting economic development in energy-scarce regions. The public-benefit model ensures that advances benefit humanity broadly rather than serving narrow corporate interests.

Key attributes of this initiative include:

• Accelerated Research: Compression of decades of traditional fusion research into years
• Clean Energy Production: Minimal radioactive waste and no risk of meltdowns
• Universal Access: Democratic distribution of technological advances
• Sustainable Development: Support for global economic growth without environmental degradation

The combination of GNoME and the MQ chip represents a paradigm shift in fusion research, moving from empirical trial-and-error to data-driven optimization. This systematic approach, coupled with a commitment to public benefit, establishes a framework for achieving practical fusion power while ensuring equitable access to its benefits.

Conclusion The integration of GNoME and the MQ chip offers a promising path toward achieving practical nuclear fusion. By combining advanced AI-driven materials discovery with quantum-enhanced simulations, this initiative addresses key technical challenges while maintaining a focus on global benefit. Success would revolutionize energy production, supporting sustainable development and environmental preservation for future generations.