A Quantum Analog to Basis Function Networks
Dan A. Ventura · AIP conference proceedings · 2002
A Fourier‐based quantum computational learning algorithm with similarities to classical basis function networks is developed. Instead of a Gaussian basis, the quantum algorithm uses a discrete Fourier basis with the output being a linear combination of the basis. A set of examples is considered as a quantum system that undergoes unitary transformations to produce learning. The main result of the work is a quantum computational learning algorithm that is unique among quantum algorithms as it does not assume a priori knowledge of a function f.