Could quantum computers finding global minima fundamentally change neural network capabilities? Discussion about optimization’s role in AI capability

In light of Google’s recent Willow quantum chip announcement, I’ve been thinking about some potentially profound implications for AI development. Would love to hear thoughts on this theoretical direction.

The Core Idea: Global Minima and AI Capability

Current neural networks achieve impressive results while likely operating at local minima due to classical computing limitations. But what if quantum computers could reliably find global minima?

1. Our human brains are neural networks optimized by basic biochemical processes and evolution
2. Current AI systems already outperform humans in many domains while potentially being stuck in local minima
3. Quantum computers might be able to find truly optimal configurations that neither biological nor classical systems can reach

Think about it this way: If human intelligence emerges from neural networks optimized by basic biochemical processes, then neural networks optimized by quantum computing should be capable of something far beyond human intelligence.

Humans don’t have quantum annealing to solve our neural networks in our brains?

The Loss Function Question

This leads to an even more interesting possibility: Could we use quantum computing to search for optimal loss functions themselves?

Current loss functions are likely simplified for computational tractability, and we use various hacks and tricks to compensate for this simplification. But quantum computers could potentially:

• Explore loss function space exponentially faster
• Find counterintuitive formulations that classical computers miss
• Handle many more variables and interactions
• Define optimality in ways we haven’t considered

Imagine using quantum systems themselves to define what “optimal” means, similar to how quantum systems in nature find their minimal energy states.

Questions This Raises

1. How much better could a truly globally optimal neural network perform?
2. Could this represent a fundamental leap in capability rather than just an incremental improvement?
3. Are we underestimating the importance of optimization quality versus just scaling up models?
4. Could quantum-derived loss functions reveal fundamental principles about intelligence and optimization?

Would love to hear others’ thoughts on this. Am I missing something obvious, or could this be a meaningful direction for future AI development?

Edit: This is meant as a theoretical discussion. I understand current quantum computers have significant limitations and practical challenges.