Quantum Random, Self-Modifiable Computer

We describe a computing machine, called an ex-machine, that uses self-modification and randomness to enhance the computation. The name ex-machine is derived from the latin extra machinam because its can evolve as it computes so that its complexity increases without an upper bound. In an embodiment, an ex-machine program can compute languages that a Turing or standard machine cannot compute. In an embodiment, the ex-machine has three types of instructions: standard instructions, meta instructions and random instructions. In an embodiment, the meta instruction self-modify the machine as it is executing so that new instructions are added. In an embodiment, the standard instructions are expressed in the C programming language or VHDL dataflow language. Random instructions take random measurements from a random source. In an embodiment, the random source produces quantum events which are measured.In an embodiment, an ex-machine receives a computer program as input, containing only standard instructions. In an embodiment, the ex-machine combines its random instructions and its meta instructions to self-modify the ex-machine instructions, so that it can evolve to compute (i.e., verify) the correctness of the computer program that it received as input. In an embodiment, an ex-machine uses its meta instructions and random instructions to improve its machine learning procedures as the ex-machine is computing.In an embodiment, machine computation that adds randomness and self-modification to the standard digital computer instructions has more computing capability than a standard digital computer. This capability enables more advanced machine learning procedures where in some embodiments meta instructions 1 and random instructions improve the machine learning procedure, as it is executing. In an embodiment, differential forms, the curvature tensor, and curvature of saddle points are used to help self-modify and improve an initial, standard gradient descent method.