This paper presents the approach of the quantum complex-valued backpropagation neural network or QCBPN. The challenge of our research is the expected results fiom the development of the quantum neural network using complex-valued backpropagation learning algorithm to solve classification problems. The concept of QCBPN emerged fiom the quantum circuit neural network research and the complex-valued backpropagation algorithm. We found that complex value and the quantum states share some natural representation suitable for the parallel computation. The quantum circuit neural network provides a qubit-like neuron model based on quantum mechanics with quantum backpropagationlearning rule, while the complex-valued backpropagation algorithm modifies standard backpropagation algorithm to learn complex number pattem in a natural way. The quantum complex-valued neuron model and the QCBPN learning algorithm are described. Finally, the realization of the QCBPN is exploited with a simple pattem recognition problem.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.