Automatic Quantum Computer Programming: A Genetic by Lee Spector

By Lee Spector

Once learned, the possibility of large-scale quantum desktops offers to considerably rework laptop technology. regardless of large-scale foreign efforts, despite the fact that, crucial questions about the possibility of quantum algorithms are nonetheless unanswered. automated Quantum machine Programming is an creation either to quantum computing for non-physicists and to genetic programming for non-computer-scientists. The e-book explores a number of ways that genetic programming can aid computerized quantum machine programming and offers particular descriptions of particular suggestions, in addition to numerous examples in their human-competitive functionality on particular problems.

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1. What is Genetic and Evolutionary Computation? The phrase "genetic and evolutionary computation" is used in the literature to describe a wide array of computational enterprises that borrow general principles from genetics and from evolutionary biology. The motivations for these enterprises vary considerably. Some researchers are primarily interested in the processes that underlie biological genetics and evolution, and they use computational models (which may include problem-solving components) as tools to develop, test, and refine biological theory.

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.

5. At generation 22, a perfect match to the target function is found. Obtaining Genetic Programming Results Genetic programming is a general technique that can be applied, without substantial re-engineering, to a wide array of problems. The preparatory steps that one must complete to apply the technique to a new problem include the selection and/or definition of appropriate functions and Genetic Programming 53 terminals out of which programs will be constructed, the definition of a problem-specific fitness function, and the setting of other parameters such as population size and mutation and crossover rates.

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