Accelerated learning in neural networks incorporating quantum unitary noise and quantum stochastic rounding using silicon based quantum dot arrays

Abstract

A novel and useful system and method of accelerated learning in neural networks using silicon based quantum dot arrays. Unitary noise is derived from a probability of detecting a particle within a quantum dot array structure comprising position based charge qubits with two time independent basis states |0> and |1>. A two level electron tunneling device such as an interface device, qubit or other quantum structure is used to generate quantum noise. The electron tunneling device includes a reservoir of particles, a quantum dot, and a barrier that is used to control tunneling between the reservoir and the quantum dot. Controlling the bias applied to the barrier controls the probability of detection. Thus, the probability density function (PDF) of the output unitary noise can be controlled to correspond to a desired probability. The quantum unitary noise is injected into one or more layers of an artificial neural network (ANN) to improve the learning and training process. The quantum noise source is also used to perform stochastic rounding in the ANN. The PDF of the quantum noise source output is set to a desired value in accordance with the remainder portion of input numbers within the layers of the ANN to be rounded.