We employ a quantum formalism and a unitary representation of the Rubik's Cube, using Deep Reinforcement Learning to navigate Hamiltonian operators for potential quantum solutions.

I'm used to thinking of Mr. Turing as a mathematician deeply interested in automating computation, less so as a programmer slogging out code. It's heartwarming to see his hard-won tips to other programmers from ~1950, and they are remarkably relevant today. e.g....

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A balance must always be struck between the following incompatible desires:

  * To carry the process through as fast as possible

  * To use as little storage space as possible

  * To finish the programming as quickly as possible

  * To achieve the maximum possible accuracy

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1. Make a plan 2. Break the problem down 3. Do the programming of the new subroutines 4. Program the main routine

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