Make a complete graph where the cost of the edge between $$$u$$$ and $$$v$$$ is the cost as you wrote in the starement. The answer is the cost of minimum spanning tree of the graph.

I like the tag 'hevi problem'（´∀｀　）

I'm not sure why they gave the cost in that way, it's the xor between P and Q — p|q contains each power of 2 that appears in any of the two numbers, p&q contains those that appear in both. When subtracting, you are left with only those that appear in one of the two

>>> s = Solver()
>>> a = BitVec('a', 64)
>>> b = BitVec('b', 64)
>>> s.add((a|b)-(a&b) != a^b)
>>> s.check()
unsat

This is a bit overkill for the given constraints, but if M is the maximum value in A, the problem can be solved in O(N*log^2(M)) using divide-and-conquer.

If A has fewer than two unique elements, the answer is 0. Otherwise, find the highest bit where any two elements of A differ. Then, divide A into two arrays B and C, where B contains all elements of A with a 0 in that bit and C contains all elements of A with a 1. Solve the problem recursively on those two arrays. Then, construct a bitwise trie out of B and, for each element c of C, find the element b of B minimizing b XOR c. This can be done by searching for b in the trie, going down the opposite branch from each node where b's branch does not exist. The answer to the problem can be calculated as the sum of the two subproblems' solutions plus the minimum value of b ^ c plus a number with only the current bit set. Each level of the divide-and-conquer takes O(N*log(M)). There are O(log(M)) levels, meaning the overall complexity is O(N*log^2(M)).

As other comments pointed out, the problem comes down to finding the minimum spanning tree cost in a complete graph of elements of A, where edge weights are the bitwise XOR of their nodes. Note that the minimum spanning tree can be constructed by considering edges in order of nondescending weight. Since the cost of each edge is the XOR of its vertices, edges connecting two vertices that share the first k bits will be considered before edges connecting two vertices that differ anywhere in these k bits. Thus, we can simply find the MST for each subset based on its value in the first bit where any element differs and then connect these two subtrees optimally with a single edge.