<ul><li>Optimization techniques in deep learning are predominantly led by first-order gradient methodologies like SGD, but second-order optimization methods like Newton's GD can greatly benefit neural network training.</li><li>Matrix inversion is a major bottleneck for Newton's GD, with a time complexity of O(N^3).</li><li>The use of quantum linear solver algorithms (QLSAs) presents a promising approach to accelerate matrix inversion with exponentially reduced time complexity of O(d * κ * log(N * κ / ε)).</li><li>Q-Newton is a hybrid quantum-classical scheduler proposed to accelerate neural network training with Newton's GD by coordinating between quantum and classical linear solvers.</li></ul>

Q-Newton: Hybrid Quantum-Classical Scheduling for Accelerating Neural Network Training with Newton's Gradient Descent

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