Reduced parameters lower computational overhead per iteration, decreasememory usage, and may lead to faster convergence by simplifying
optimization landscapes.
The solver optimizes quantum gate parameters using data-driven classicalalgorithms (e.g., gradient-based optimization), creating a hybrid loop wherequantum circuits process data and classical systems update parameters.
Early studies show QA-PINNs achieving comparable accuracy to classicalPINNs with 10-100x fewer parameters in specific tasks (e.g., fluid dynamics),though results vary by problem and hardware.
While quantum systems face challenges in data loading, hybrid approaches(e.g., quantum embedding + classical preprocessing) and quantum parallelism may enhance scalability for specific tasks like high-dimensional simulations.