# Variational Quantum Circuits for Machine Learning.
## 5 Results
Finally, a success rate of 80.85% was obtained after its execution using the classical algorithm. These results are shown in Figure 13 where class A is 'no weak signal' and class B is 'weak signals'. Another test was performed using a sample of 47 items but only two classes were considered: \"weak signals\" and \"no weak signals\". The latter includes both noise and strong signals. The training set was composed of 33 items classified as \"no weak signals\" and 14 items classified as \"weak signals\". When the system was used for a test set of 11 items classified as \"noise\" and 1 item classified as \"weak signals\", a 70.21% success rate was obtained after its execution in 'ibmq_santiago'. A success rate of 74.47% was obtained after its execution using qiskit quantum simulator. Finally, a success rate of 80.85% was obtained after its execution using the classical algorithm. These results are shown in Figure 13 where class A is 'no weak signal' and class B is 'weak signals'. Figure 12: Result of applying quantum SVM algorithm to the input data for three classes where prediction1 is classical computer, prediction2 is quantum simulation and prediction3 is quantum real computer. points that are classified as 'no weak signals' including both 'noise' and'strong signals'. The results show that the classical computer obtained the highest success rate while the quantum simulator obtained a slightly lower performance. However, the use of the real IBM quantum computer 'ibmq_santiago', obtained a success rate of 70.21%, being lower than that obtained using the quantum simulator due to the noise resulting from the available quantum devices. These results show an influence of the dataset on the performances, to which obtaining a suitable feature map is key. In addition, as stated in [38], available quantum classifiers do not offer a considerable advantage when the feature vector kernel can be efficiently obtained on a classical computer. In fact, this same source reveals that it is possible to achieve 100% performance if the optimal feature map is available. In future research, it is necessary to further study the potential for improvement if suitable feature maps are detected for other real datasets. Although quantum computing seems to bring many advantages to machine learning algorithms, the results of this study show that with large datasets both the simulator and the real quantum computer have been unable to present results in less time than a classical computer. The need to encode the data in a quantum state and the need to run each circuit a large number of shots as a measure of robustness to noise confirm that current quantum technology cannot yet be considered an alternative to classical computing [59]. The four main problems that need to be solved are:  
1. First, encoding the input data is especially complex when compared to data processing, and this complexity may come at a high cost to quantum algorithms, which may lose some of their fast-processing advantage. 2. Second, the output measurements are not easily interpretable, since qubit entanglement computes many solutions until the states collapse into a particular final state. 3. Third, quantum computation is thought to offer an advantage to solve large problems, but due to hardware limitations, this has not yet been demonstrated. 4. Finally, better algorithm evaluation systems are needed to appreciate the advantages of quantum algorithms over classical algorithms. Some possible sources of improvement have been presented in Section 3. Quantum convolutional neural networks are giving good results to complex problems and have been shown to be more robust to noise than other quantum alternatives [52]. Quantum simulator success rates or similar are expected to be attained by the reduction of noise. On the other hand, the integration of quantum variational circuits in reinforcement learning techniques allows higher efficiencies to be achieved [60]. Another problem that has been detected is that all these applications are centralized.