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Quantum machines+Machine Learning = Dynamic Duo 4 Data (Part 1)

Quantum computing and machine learning are two big emerging technologies of the 21st Century. While these two technologies are independent, they can leverage one another. Especially when it comes to Quantum computing helping to advance machine learning. Machine learning is a very computationally intensive process and therefore the more powerful the machine that you use in developing the algorithms the better. Let’s look at how quantum computing can help with developing better machine learning algorithms:


How does quantum computing help with machine learning?


Machine learning algorithms require information to be fed into the machine in order for it to learn and become better at making predictions, identifying patterns etc. The amount of information that can be fed into the algorithms and processed is limited by the processing power of the machines that you use for this process. With modern day computers we are limited by how quickly we can do this but with quantum computing this process will be much faster than anything we have experienced thus far. If you just consider the amount of data that we create everyday (1.145 trillion MB per day), we are going to need more powerful machines to handle and process all of that information. This is where the overlap between quantum computing and machine learning can be very useful in allowing us to analyze all of this information and find new insights based on that information.


Source @ eduCBA


How is machine learning applied in cybersecurity?


Machine learning in cybersecurity is primarily used when it comes to analytics. For example in your antivirus or firewall solutions, machine learning is used to teach these tools how to identify behaviour that suggests that a program may be malware. This way these tools can detect and block malware that wasn’t previously known to the software by its signature and help prevent attacks before they can start. As machine learning algorithms mature, these tools will become more accurate in their assumptions and result in less false positives and less false negatives. Some examples of cybersecurity companies that are well known for integrating AI/machine learning in their solutions are darktrace, crowdstrike, vade secure and webroot.


Source @ Data Flair


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