Non-binary artificial neuron with phase variation implemented on a quantum computer
This work represents an incremental step toward efficiently implementing artificial neural networks on near-term quantum devices.
The authors tackled the limitation of binary quantum neuron models by introducing a non-binary artificial neuron that manipulates phase to handle continuous values on a quantum computer, demonstrating through simulations that it can work with gradient descent in a hybrid training scheme.
The first artificial quantum neuron models followed a similar path to classic models, as they work only with discrete values. Here we introduce an algorithm that generalizes the binary model manipulating the phase of complex numbers. We propose, test, and implement a neuron model that works with continuous values in a quantum computer. Through simulations, we demonstrate that our model may work in a hybrid training scheme utilizing gradient descent as a learning algorithm. This work represents another step in the direction of evaluation of the use of artificial neural networks efficiently implemented on near-term quantum devices.