## 1 Introduction  
Quantum neural networks (QNNs) is a research domain that proposes neural network models based on quantum computing postulates [1]. One of the main advantages of classical artificial neural networks (CANNs) is its ability to perform computations in parallel [2]. However implementing CANNs on traditional computers does not offer this advantage. Traditional computers allow computations based on the bit value that is either \"0\" or \"1\". [PERSON] suggested that performing that quantum computation is also perfectly possible at the quantum bit (qubit) level [3] on the grounds of the superposition phenomenon. In fact, quantum computations are based on quantum correlations [4; 5; 6; 7], particularly, quantum entanglement [8]. Therefore, the learning phase is expected to be much faster than CANNs due to quantum parallelism. The birth of QNNs was in 1995 with [PERSON] who tried to link CANNs to quantum mechanics [9]. Consequently, several researchers proposed various models and algorithms of CANNs that attempt to harness the principles of quantum computing and quantum information theory [1]. These algorithms are classified into three main categories. The first category is called QNN's algorithms [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21]. They are based on quantum mechanics postulates [1] and can be implemented only on quantum computers. The second category is ANNs that are inspired by quantum computing, and exploits some mathematical aspects of quantum information and quantum computation to speed up the learning process of CANNs [22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34]. However, this kind of ANNs can be implemented only on classical computers rather than physical quantum computers. Finally, the third category of these algorithms is a hybrid model that combines QNNs and CANNs in such a way that some calculations are performed on quantum computers, then based on the outputs of measurement operations, the remaining calculations are performed on classical computers [35; 36; 37; 38]. Competitive learning is one of the unsupervised learning techniques that is inspired by biological brains [2]. This type of learning allows for competition among neurons, and ends with a single winner neuron. The winning neuron has the most similar pattern to an input test pattern. Because this competition allows only one neuron to be a winner, it is called winner-take-all competition. This competition is often used in Hamming neural networks [2; 39] which is used to solve some classification problems intractable for other CANNs. There are four models proposed to implement quantum competitive learning, two of these models are not applicable on physical quantum systems [23; 38], while the other two models have unreliable results [21; 40]. To overcome the defects of the previous models, we exploit the power of quantum computing to propose a novel quantum classifier by harnessing both the superposition and entanglement to implement competitive learning on quantum systems. In this paper, we propose a novel quantum classification model that exploits the superposition property to allow the competition between the neurons by applying the CNOT-gate, between the register of the input pattern and the register that stores prototypes patterns, and applying the quantum NOT-gate on the register that stores prototypes patterns. Then the Toffli-gate is used to mark the wining neuron that represents that class. This winning neuron could have zero value state, \(|0\rangle\), or one value state, \(|1\rangle\). So, in order to find the value of the wining neuron, we need to implement the winner-take-all technique. To implement this technique, we propose to use one of the entanglement measures called concurrence, so that if the wining neuron has zero value state \(|0\rangle\), then the corresponding value of concurrence is zero. Conversely, if the wining neuron has one value state \(|1\rangle\), then the value of concurrence is one.