A quantum genetic algorithm with quantum crossover and mutation operations

In the context of evolutionary quantum computing in the literal meaning, a quantum crossover operation has not been introduced so far. Here, we introduce a novel quantum genetic algorithm which has a quantum crossover procedure performing crossovers among all chromosomes in parallel for each generation. A complexity analysis shows that a quadratic speedup is achieved over its classical counterpart in the dominant factor of the run time to handle each generation.Comment: 21 pages, 1 table, v2: typos corrected, minor modifications in sections 3.5 and 4, v3: minor revision, title changed (original title: Semiclassical genetic algorithm with quantum crossover and mutation operations), v4: minor revision, v5: minor grammatical corrections, to appear in QI

Quantum Logic circuits for solid-state quantum information processing

This thesis describes research on the design of quantum logic circuits suitable for the experimental demonstration of a three-qubit quantum computation prototype. The design is based on a proposal for optically controlled, solid-state quantum logic gates. In this proposal, typically referred to as SFG model, the qubits are stored in the electron spin of donors in a solid-state substrate while the interactions between them are mediated through the optical excitation of control particles placed in their proximity. After a brief introduction to the area of quantum information processing, the basics of quantum information theory required for the understanding of the thesis work are introduced. Then, the literature on existing quantum computation proposals and experimental implementations of quantum computational systems is analysed to identify the main challenges of experimental quantum computation and typical system parameters of quantum computation prototypes. The details of the SFG model are subsequently described and the entangling characteristics of SFG two-qubit quantum gates are analysed by means of a geometrical approach, in order to understand what entangling gates would be available when designing circuits based on this proposal. Two numerical tools have been developed in the course of the research. These are a quantum logic simulator and an automated quantum circuit design algorithm based on a genetic programming approach. Both of these are used to design quantum logic circuits compatible with the SFG model for a three-qubit Deutsch-Jozsa algorithm. One of the design aims is to realise the shortest possible circuits in order to reduce the possibility of errors accumulating during computation, and different design procedures which have been tested are presented. The tolerance to perturbations of one of the designed circuits is then analysed by evaluating its performance under increasing fluctuations on some of the parameters relevant in the dynamics of SFG gates. Because interactions in SFG two-qubit quantum gates are mediated by the optical excitation of the control particles, the solutions for the generation of the optical control signal required for the proposed quantum circuits are discussed. Finally, the conclusions of this work are presented and areas for further research are identified

Evolving quantum circuits and programs through genetic programming

Abstract. Spector et al. have shown [1],[2],[3] that genetic programming can be used to evolve quantum circuits. In this paper, we present new results in this field, introducing probabilistic and deterministic quantum circuits that have not been previously published. We compare our techniques with those of Spector et al, and point out some differences in perspective between our two approaches. Finally, we show how, by using sets of functions rather than precise quantum states as fitness cases, our basic technique can be extended to evolve true quantum algorithms

Evolving Quantum Circuits and Programs through Genetic Programming

Abstract. Spector et al. have shown [1],[2],[3] that genetic programming can be used to evolve quantum circuits. In this paper, we present new results in this field, introducing probabilistic and deterministic quantum circuits that have not been previously published. We compare our techniques with those of Spector et al, and point out some differences in perspective between our two approaches. Finally, we show how, by using sets of functions rather than precise quantum states as fitness cases, our basic technique can be extended to evolve true quantum algorithms