|Presentation Date||October 18, 2012|
|Topic(s)||quantum algorithms in machine learning|
Many machine learning algorithms are NP complete, and as such require large server farms to operate if at all. Quantum algorithms seem to solve NP complete problems in polynomial time, and as such provide a better alternative to current systems. Algorithms such as Shor's algorithm which factors prime numbers or Grover's algorithm which searches an unsorted database in O(N)^1/2 time can improve our current machine learning algorithms. An example of how that might be possible is shown by Google through their creation of a binary classifier which recognizes images of cars in digital images and outperforms its non-quantum counterparts.
If a strong enough quantum machine can be constructed, it will almost certainly take the current computing industry by storm.