Quantum Adaptive Learning Rate

The training of Neural Networks is a compute intensive task that in current classical implementations relies on gradient descent algorithms and a certain learning rate that controls the granularity of the search for a solution. This paper explores a new hybrid quantum-classical approach, which is not only novel for exploding quantum computing to partially solve the problem, but also for being the first approach that adjusts the learning rate with exact information pertaining to the solution of this training problem. The Quantum Adaptive Learning Rate approach is tested in a proof of concept classification problem. Key aspects of the practical implementation of the Harrow, Hasidim and Lloy (HHL) quantum algorithm are discussed.