# Variational Quantum Circuits for Machine Learning.
## 4 Materials and Methods
### Application to the Detection of Weak Signals of the Future  
Weak signals are indicators of incipient changes that will have a future impact [67]. Even for experts, the detection of these events is complicated as their impact is as yet unpredictable.  
The available studies on these types of signals are mostly qualitative [68], valid only for specific applications [69; 70; 71] and based on the opinion of experts and other stakeholders [72]. Even in cases where quantitative analysis is used, these studies are based only on structured data [73].  
However, a system has been developed to assist experts in the detection of weak signals that are quantitative, multipurpose, and drawn from various types of unstructured data sources. This system has been successfully applied in various sectors such as solar panels [74], artificial intelligence [75] and remote sensing [44].  
The data used by the system are all kinds of documents from a variety of sources, such as scientific articles, newspaper articles and social media posts. One of the most important phases of the system is text mining, in which all the words in these documents are classified into three different data classes as can be seen in Figure 7, where data are classified in three different classes: (i) possible weak signals, (ii) strong signals (those signals that have already reached the necessary impact to be known by a wide audience), and (iii) noise (words and expressions in which there has been no increase in usage). The two main factors are the average frequency of each word and a time weighted _increase_ rate to give more value to the most recent occurrences.  
Figure 6: Model circuit U\({}_{\theta}\) that maps used for the classifier.  
Two different keyword maps are created with this structure. The first one is based on the 'degree of diffusion' or _DoD_, and the second one is based on the 'degree of visibility' or _DoV_. These two dimensions are part of the model of a weak signal by [PERSON] [76].  
The degree of diffusion (_DoD_) uses the total number of documents where the keyword appears as an average frequency to define an increasing rate considering different periods of time. The degree of visibility (_DoV_) uses the total number of appearances of each keyword (independently of the number of documents) as an average frequency, to define an increasing rate considering again different periods of time. The keyword is considered a weak signal when in both keyword maps for _DoD_ and _DoV_ it is classified as weak signal.  
In available studies, the opinion of experts [44] is used to define the thresholds that divide the word cloud into these three clusters. However, current results show that many of the expressions detected as weak signals may be false positives.  
For this reason, a machine learning system to define the clusters in an automatic way may be more appropriate.  
The process consists of creating a training set considering only those words classified as weak signals in both _DoV_ and _DoD_ maps. Then, the classifier is applied to classify the rest of the words to reduce the number of false positives detected.  
In addition, the applications in which the system has been used has had an input dataset of about 90 k documents, which implies about 150 million words. The processing advantage offered by quantum computers is a very interesting advance in the classification of these words.  
In the next section, the system is analyzed by applying the quantum classifier described in this section to the medical imaging sector.