Tackling the toughest issues using quantum machine learning
In this blog post, Tanisha Bassan, a future quantum computing engineer, tackles some of the difficulties with using quantum machine learning.
Quantum computing can be hard to understand. Where to begin? What tools to use? What’s even possible? In this blog post, Tanisha Bassan, a future quantum computing engineer, tackles some of the difficulties with using quantum machine learning.
Beginning with classical machine learning, Bassan breaks down the three different areas of machine learning, with a focus on supervised learning. Classical computers are great at classifying between two different classes, but often struggle when it comes to multiple, different classes. But fear not, that’s what quantum computing is for.
Quantum computers can help with classifying objects in nth dimensions that are extremely hard for classical computers. When put together, quantum computers and machine learning have the power to design new chemical compounds.
But, that’s not all. Support vector machines, supervised learning algorithms used for classification and regression problems, can also classify some pretty complex data.
Want to learn more?
Head on over to Bassan’s blog post, Tackling the World’s Hardest Problems Using Quantum Machine Learning, to learn more about using support vector machine algorithms as well as her personal project and code.