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 == Applications ==
 Optimization of complex problems is one of the most promising areas for quantum computing in several research areas:
-* Machine learning: quantum computing can reduce training and processing times;
+* Machine learning: quantum computing could reduce training and processing times;
-* Finance: quantum computing can speed up portfolio risk assessment and fraud detection;
+* Finance: quantum computing could speed up portfolio risk assessment and fraud detection;
-* Molecular modelling: quantum computing can simulate more complex chemical systems and simulate reactions in real time;
+* Molecular modelling: quantum computing could simulate more complex chemical systems and simulate reactions in real time;
-* Meteorology: quantum computing can increase the number of variables for weather forecasting;
+* Meteorology: quantum computing could increase the number of variables for weather forecasting;
-* Logistics: quantum computing can optimize logistics and workflow planning associated with supply chain management.
+* Logistics: quantum computing could optimize logistics and workflow planning associated with supply chain management.
 == Software ==