## 6 Application  
To evaluate the efficiency of the proposed algorithm, we conducted a fair comparison with some of other reported state-of-the-art classification algorithms to classify the state of Reactor Coolant Pump (RCP) in a risky Nuclear Power Plant (NPP). In the case of plant transients, the process of optimizing the outage gets more complex based on the neutronic parameters inside the reactor's core, and the thermal-hydraulic processes of primary and secondary coolant loops that are governing all the reactor's processes. These processes produce a massive stream of data that is processed simultaneously in a short time. Three Mile Island (TMI) accident is a typical example to illustrate the interference among the massive number of signals [61]. In TMI accident, the Loss Of Coolant Accident (LOCA) caused 500 alarms to appear in the first minute and 800 in the second-minute [61]. Instantly, the large number of alarms affects the psychological control of the human operator, and consequently his/her ability to reach the correct decision will be impaired. This leads to the operator's inability to determine the source of the failure precisely. To minimize the interference caused by these alarms, attention has been given to provide an intelligent support system [62]. This has been applied in a crucial partof the reactor's operation, i.e Reactor Coolant system (RCS) which is the primary cooling system in NPPs. RCS consists of four major components: a reactor vessel, the steam generator (SG), RCP and pressurizer. Here, the efficiency of the proposed QCPNN algorithm is validated via classifying the state of RCP in a risky NPP. The state of RCP is characterized via 12 sensors that are reported in Table 1. According to the history that was recorded in real time during the accident of the Kori-2 PWR, these sensors transmitted fault signals that were recorded via 41 patterns of different faults. In this experiment, these patterns are used as prototypes to classify various faults into two type of classes: the high risk and low-risk mode. The current application requires 26 qubits of a real quantum computer which it is not available for research so far. So, the experiments are simulated using Java tool on a PC with a configuration 3.1 GHz Intel(R) Xeon(R) E5-2687 v3, and 64.00 GB RAM on 64-bit OS operating system. We have considered testing the classification accuracy when the patterns of alarms may appear incomplete assuming that there is sensors failure. The classification rate of QCPNN is compared with [PERSON]'s quantum competitive neural network (QCNN) approach [21], where both techniques are tested under unified experimental conditions. Since the outcome of both models is stochastic, so we conducted the experiments for 30 runs, each run consisted of one batch of simulations, and yielded one value for the classification rate. The classification rate is defined as follows:  
\[Classification\ rate=\frac{t_{p}+t_{n}}{t_{p}+f_{p}+f_{n}+t_{n}}\,\]  
where \(t_{p}\), \(f_{p}\), \(t_{n}\), and \(f_{n}\) indicate the true positive, false positive, true negative and false negative, respectively. The mean and standard deviation of the classification rate for the proposed model (QCPNN), and [PERSON]'s model (QCNN) are reported in Figure 5. In this figure, red bars indicate mean values averaged over \(N=30\) runs, error bars indicate the corresponding standard deviations. It is clear from Figure 5 that the classification rate of the QCPNN algorithm based on entanglement measure outmatches the QCNN algorithm. Where, QCNN achieved a classification rate of 3.1% with standard deviation 2.3%. Conversely, QCPNN achieved 51.0% with standard deviation 2.4% in the classification rate. Then, we performed t-test between the results of the two models, we got \(p\text{-value}=3.1753\times 10^{-34}\), which is \(<0.001\), so the difference is highly significant.