Greetings, programming enthusiast! In this unit, we're embarking on a thrilling numerical quest, where unidentified bridges connect the islands of data. On these bridges, we'll see hashes and bins, all converging into sets! Throughout our journey, we'll utilize the fundamental concepts of Python's built-in data type, the set, to formulate an optimal solution. So, fasten your seatbelt and get ready to solve problems!

The task for this unit is to devise a Python function that accepts two lists containing unique integers and returns another list containing the elements common to both input lists. This task provides an intriguing perspective on deriving similarities between two data sequences, a scenario commonly encountered in data comparisons and analytics.

For illustration, suppose we're given two lists:

The common_elements(list1, list2) function should comb through these arrays of integers and extract the common elements between them.

The expected outcome in this case should be:

Before we delve into the optimized solution, it is instrumental to consider a basic or naïve approach to this problem and analyze its complexity. Often, our first intuitive approach is to iterate over both arrays in nested loops and find common elements. This way, for each element in the first list, we check for its presence in the second list. If it's found, it is added to our result set. Let's see how such a solution would look like: