By Lee Spector
Automatic Quantum laptop Programming presents an advent to quantum computing for non-physicists, in addition to an advent to genetic programming for non-computer-scientists. The ebook explores a number of ways that genetic programming can help automated quantum desktop programming and offers certain descriptions of particular thoughts, besides a number of examples in their human-competitive functionality on particular difficulties. resource code for the author’s QGAME quantum laptop simulator is incorporated as an appendix, and tips that could extra on-line assets provide the reader with an array of instruments for computerized quantum laptop programming.
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Extra info for Automatic Quantum Computer Programming: A Genetic Programming Approach
We provide a concise introduction to the basic concepts of genetic programming, a detailed example, and a discussion of the steps that one must generally take to obtain and understand useful results from a genetic programming system. The techniques described in this chapter are not specific to quantum computing; we will narrow the focus further to genetic programming for quantum computers in Chapter 7, following the description, in Chapter 6, of advanced genetic programming techniques that are particularly useful for evolving quantum programs.
For example, recent advances in genetic programming techniques use mechanisms derived from DNA dynamics, learning mechanisms in neural networks, immune systems, regulatory networks, and biological gene expression processes (Spector, 2003). A few of these advances, in particular some of those related to the development and evolution of modular structures, will be discussed in later chapters of this book. 1. We begin with a population of random individuals. In a problem-solving context an "individual" is usually a candidate solution — something selected from the (normally immense) set of the kinds of things that could possibly be solutions.
As such they are ideal for 42 AUTOMATIC QUANTUM COMPUTER PROGRAMMING exploring domains about whicli we have little prior knowledge. This makes them very powerful tools in the engineer's toolkit. But by the same token genetic and evolutionary computation technologies, like all so-called weak methods, will generally underperform specialized methods that are based on a deep understanding of a particular problem area's search space. When one knows a domain sufficiently well one can often develop problem-solving methods that are considerably more efficient than random variation and selection.