By Lee Spector
Automatic Quantum laptop Programming offers an advent to quantum computing for non-physicists, in addition to an advent to genetic programming for non-computer-scientists. The e-book explores numerous ways that genetic programming can aid automated quantum desktop programming and provides particular descriptions of particular options, besides numerous examples in their human-competitive functionality on particular difficulties. resource code for the author’s QGAME quantum desktop simulator is integrated as an appendix, and tips that could extra on-line assets provide the reader with an array of instruments for automated quantum laptop programming.
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Extra resources for Automatic Quantum Computer Programming
In traditional genetic programming all of the constant terminals used for a particular run must be of the same data type. The functions used in the run must all return values of this same type, and must take arguments only of this type. These restrictions prevent type incompatibility errors, but they are inconvenient; several ways to relax these restrictions are discussed in Chapter 6. Additional steps must often be taken to ensure that arbitrary programs are also semantically valid — that is, that they will always execute without error, producing interpretable (even if incorrect) results.
A cube diagram of the third state in the execution of Grover's algorithm, after the apphcation of a HADAMARD gate to qubit 1. 1. A cube diagram of the fourth state in the execution of Grover's algorithm, after the application of a U-THETA gate to qubit 0. /? 8. A cube diagram of the fifth state in the execution of Grover's algorithm, after the database call. In this example the single 1 in the database is at address (0,0). 9. A cube diagram of the sixth state in the execution of Grover's algorithm, after the application of another HADAMARD gate to qubit 2.
Until this happens (or until the user gives up) the system proceeds through a loop of selection, variation, and re-assessment. The details of this "selection, variation, assessment" loop, and of the representations and algorithms used within it, vary among different forms of genetic and evolutionary computation. For example, in some ("generational") methods the entire population is assessed first and is then subjected to distinct population-wide selection and variation procedures. In other ("steady state") methods single individuals or small groups of individuals are progressed through the entire loop independently.