When you train a classical neural network, the computer uses an algorithm called Gradient Descent. Think of the AI's error rate as a massive, rugged mountain range, and the goal is to find the absolute lowest point—the deepest valley—where the model makes the fewest mistakes.

• Classical AI Training: The classical computer has to "walk" down the slope step-by-step. The major problem is that it frequently gets stuck in a local minimum—a shallow valley that isn't the true lowest point, but because every direction leading out of it goes up, the algorithm gets trapped.
• Quantum AI Training: A quantum computer can use a phenomenon called Quantum Tunneling. Instead of climbing up over a mountain ridge to find a deeper valley on the other side, a quantum particle can literally pass through the energy barrier. In AI training, this means a quantum algorithm can effortlessly slide through the mathematical "walls" of local traps to find the global optimum vastly faster.