Abstract
Various models of quantum neural networks exist imitating the powerful class of machine learning algorithms, widely applied and used in many of intelligent systems and applications. While comparative models of quantum neural networks exist, their computational complexity might require specific unitary transforms for simulating the activation function of the cell, simulation of continuous processes for learning or adding a large amount of ancilla qubits. In order to solve some of these problems, we present a quantum neural network model called CNOT Measured Network (CMN). The CMN uses only CNOT quantum gates and the measurement operator and as such is very simple to implement in any quantum computer technology. The CMN can by using only these two simple operators, result in a Turing universal operators AND and OR while keeping the learning speed optimized to the complex nature of the quantum network and a constant number of ancila qubits.