what is the time complexity for an algorithm that operations to complete grows by 4 when doubling the input length? I'm working on an algorithm and I'm trying to figure out its time complexity given the operations it takes to complete a input set of specific length, I have been testing the algorithm with varying input lengths.

The results shows that every time I double the input length, it takes 4 times more operations than before to complete:


20 items = 1M (M=million)
40 items = 4M
80 items = 16M
160 items = 64M
320 items = 256M
640 items = 1024M


What is the time complexity/running time that fits better with the above results?
Recurrent equation:
$$T(n) = 4 \cdot T(\frac{n}{2})$$
$$T(1) = O(1)$$
Its solution:
$$T(n) = \Theta(n^2)$$