SIAM Journal on Computing
Quantum computing applications of genetic programming
Advances in genetic programming
Computer arithmetic: algorithms and hardware designs
Computer arithmetic: algorithms and hardware designs
Quantum computation and quantum information
Quantum computation and quantum information
Computational Complexity, Genetic Programming, and Implications
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Evolutionary Approach to Quantum andReversible Circuits Synthesis
Artificial Intelligence Review
Improving quantum circuit dependability with reconfigurable quantum gate arrays
Proceedings of the 2nd conference on Computing frontiers
Survivability of Embryonic Memories: Analysis and Design Principles
EH '05 Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware
Automatic Quantum Computer Programming: A Genetic Programming Approach (Genetic Programming)
Automatic Quantum Computer Programming: A Genetic Programming Approach (Genetic Programming)
Evolving Hogg's quantum algorithm using linear-tree GP
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
Automatic Synthesis for Quantum Circuits Using Genetic Algorithms
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
A review of procedures to evolve quantum algorithms
Genetic Programming and Evolvable Machines
Quantum-inspired evolutionary algorithms: a survey and empirical study
Journal of Heuristics
A quantum genetic algorithm with quantum crossover and mutation operations
Quantum Information Processing
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This paper presents a new methodology for running Genetic Algorithms on a Quantum Computer. To the best of our knowledge and according to reference [6]there are no feasible solutions for the implementation of the Quantum Genetic Algorithms (QGAs). We present a new perspective on how to build the corresponding QGA architecture. It turns out that the genetic strategy is not particularly helpful in our quantum computation approach; therefore our solution consists of designing a special-purpose oracle that will work with a modified version of an already known algorithm (maximum finding [1]), in order to reduce the QGAs to a Grover search. Quantum computation offers incentives for this approach, due to the fact that the qubit representation of the chromosome can encode the entire population as a superposition of basis-state values.