## Quantum Machine Learning

#### Machine Learning Research

Researching quantum analogs of several classical machine learning algorithms (k-NN, SVM, TDA.) The Wikipedia article gives a decent overview of the area, and the following papers give a more in-depth overview of the field and detail how specific quantum machine learning algorithms can be implemented:

- An introduction to quantum machine learning by M. Schuld, I. Sinayskiy, F. Petruccione.
- Advances in quantum machine learning by Jeremy Adcock, Euan Allen, Matthew Day, Stefan Frick, Janna Hinchliff, Mack Johnson, Sam Morley-Short, Sam Pallister, Alasdair Price, Stasja Stanisic.
- Quantum algorithms for supervised and unsupervised machine learning by Seth Lloyd, Masoud Mohseni, Patrick Rebentrost.
- Quantum algorithms for topological and geometricanalysis of data by Seth Lloyd, Silvano Garnerone, Paolo Zanardi.
- Quantum support vector machine for big data classification by Patrick Rebentrost, Masoud Mohseni, Seth Lloyd.
- Quantum principal component analysis by Seth Lloyd, Masoud Mohseni, Patrick Rebentrost.
- Obtaining A Linear Combination of the Principal Components of a Matrix on Quantum Computers by Anmer Daskin.
- Quantum Algorithms for Nearest-Neighbor Methods for Supervised and Unsupervised Learning by Nathan Wiebe, Ashish Kapoor, Krysta Svore.

These are only the papers that I happened to have read at this point in time, and this is obviously only the tip of the iceberg. If you are interested in this area, I heavily suggest that you read the Wikipedia article, followed by the first two papers listed above, choosing your subsequent path of inquiry from the references thereof. It is an extremely interesting area of research!

As an added bonus, here is a video of a Google talk by Seth Lloyd about quantum machine learning.

If interested in hearing more about this research, feel free to contact me.