Quantum-Enhanced Inference of Conditional Future Probabilities with Reduced Memory Cost
The work proposes using quantum algorithms to estimate future probabilities in complex systems while reducing memory requirements. Classical methods are limited by high simulation counts and large memory states. The approach uses quantum models with constrained memory dimensions, which reduces systematic bias while maintaining a quadratic speed-up over classical amplitude estimation, showing practical benefits for simulating growing complexity.