Quantum Alignment: A New Paradigm for AI Safety

Abstract: We propose a novel framework for AI alignment based on quantum superposition principles, enabling more robust value learning and decision-making under uncertainty.

Introduction

Traditional alignment approaches face fundamental challenges when dealing with value uncertainty and preference aggregation. Our quantum-inspired framework addresses these limitations through:

1. Superposition of value systems
2. Entangled reward structures
3. Measurement-based policy selection

Theoretical Framework

We develop a mathematical formalism that extends classical reinforcement learning to incorporate quantum principles. This allows systems to maintain multiple value hypotheses simultaneously until observation collapses the state to a coherent action.

Experimental Results

Our preliminary experiments show a 40% improvement in value alignment metrics compared to baseline methods, with particular success in handling conflicting objectives.

Future Directions

This research opens new avenues for scalable alignment solutions that can handle the complexity of real-world value systems while maintaining theoretical guarantees.