Key Takeaway - Grover
Let’s summarize the techniques we have learned in Grover's Algorithm and hint at potential use cases for them.
We'll cover the following...
This chapter has two key takeaways. The first follows from something we learned in the previous chapter: the usage of oracles. While explaining the DJ Algorithm, we learned that oracles were powerful tools for solving problems. We encountered them here once again as a critical subroutine in Grover’s Algorithm, proving the point. There is an important distinction to point out in Grover’s oracles. In this algorithm, we realized that creating an oracle that checked a searched solution was easier than creating an oracle that found a solution.
The second technique we learned here was amplitude amplification. In it, we minimized the probability of unwanted states and maximized the probability of desired states. Another thing we can do is to augment the algorithm to negate invalid states that are not in our original search problem as well. This will speed up the algorithm and further maximize the probability of ending up at the desired state. We encourage you to investigate these extensions in your own time further.
In terms of practical applications, unstructured search is a direct application of Grover’s Algorithm. But the same idea of oracle formation and amplitude amplification can be applied to other optimization problems. In the previous lesson, we linked to the quantum analog of ...