Download e-book for iPad: Automatic Quantum Computer Programming: A Genetic by Lee Spector

February 27, 2018 | Algorithms | By admin | 0 Comments

By Lee Spector

ISBN-10: 038736496X

ISBN-13: 9780387364964

ISBN-10: 0387367918

ISBN-13: 9780387367910

ISBN-10: 1402078943

ISBN-13: 9781402078941

ISBN-10: 1402078951

ISBN-13: 9781402078958

Once learned, the opportunity of large-scale quantum pcs gives you to extensively remodel computing device technological know-how. regardless of large-scale foreign efforts, besides the fact that, crucial questions on the potential for quantum algorithms are nonetheless unanswered. computerized Quantum computing device Programming is an creation either to quantum computing for non-physicists and to genetic programming for non-computer-scientists. The e-book explores numerous ways that genetic programming can aid computerized quantum desktop programming and provides specified descriptions of particular concepts, in addition to a number of examples in their human-competitive functionality on particular problems.

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Similar strategies can be employed to produce "protected" versions of other functions with pathological special cases. 3. Traditional Genetic Operators The most common forms of genetic programming mutation involve the replacement of an arbitrarily chosen subprogram of with a newly generated random subprogram. For example, consider the following program: (+ (* X Y) (+ 4 (- Z 23))) If we wish to mutate this program we first select a random subprogram for replacement, as follows: (+ l(* X Y)| (+ 4 (- Z 23))) We then generate a new random subprogram and insert it in place of the selected subprogram: (+ (- (+ 2 2) Z) (+ 4 (- Z 2 3 ) ) ) There is plenty of room for variation of this basic scheme for mutation and many variants have been explored.

Alternatively, one could simulate the quantum computer at a higher level of abstraction, ignoring implementation details and working only with "perfect" unitary matrices. If one wishes to simulate the execution of arbitrary sequences of quantum gates then one necessarily faces exponential space and time costs whether one works at the implementation level or at a more abstract level. That is, if the number of qubits in the system is A^, then the space and time requirements for simulation will both scale approximately as 2^.

Average expected oracle calls: 1. Note that the "output" of the database is not even consulted after the database query; instead, the answer is decoded from the states in which Quantum Computer 33 Programming the input qubits are left. This highhghts a counter-intuitive property of many quantum algorithms, sometimes called the "back action" of unitary gates. 13 illustrate the action of this algorithm via cube diagrams for the single case of a database with the item stored at the address (0,0).

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Automatic Quantum Computer Programming: A Genetic Programming Approach (Genetic Programming) by Lee Spector


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