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Genetic and Evolutionary Computation Conference (GECCO)

Quantum evolutionary programming

Bart Rylander, Terry Soule, James A. Foster, Jim Alves-Foss

Published Jul 7, 2001• 28 citations
OpenAlex

Abstract

Recent developments in quantum technology have shown that quantum computers can provide dramatic advantages over classical computers for some problems [1] [2]. These quantum algorithms rely upon the inherent parallel qualities of quantum computers to achieve their improvement. In this paper we provide a brief background of quantum computers. We present a simple quantum approach to genetic algorithms and analyze its benefits and drawbacks. We describe the quantum advantage of true randomness. We show that in some cases, such as program induction, there is a measurable difference [3]. These algorithms are significant because to date there are only a handful of quantum algorithms that take advantage of quantum parallelism [4] and none that show an advantage due to true randomness. Finally, we provide ideas for directions of future research.

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International Journal of Computer Applications•Sep 18, 2014•17 citations
Recent developments in quantum technology have shown that quantum computers can provide dramatic advantages over classical computers for some problems [1] [2]. These quantum algorithms rely upon the inherent parallel qualities of quantum computers to achieve their improvement. In this paper we provide a brief background of quantum computers. We present a simple quantum approach to genetic algorithms and analyze its benefits and drawbacks. We describe the quantum advantage of true randomness. We show that in some cases, such as program induction, there is a measurable difference [3]. These algorithms are significant because to date there are only a handful of quantum algorithms that take advantage of quantum parallelism [4] and none that show an advantage due to true randomness. Finally, we provide ideas for directions of future research.
Recent developments in quantum technology have shown that quantum computers can provide a dramatic advantage over classical computers for some algorithms. Since most problems of real interest for genetic algorithms (GAs) have a vast search space [Holland, 1975], it seems appropriate to consider how quantum parallelism can be applied to GAs. In this paper we present a simple quantum approach to genetic algorithms and analyze its benefits and drawbacks. This is significant because to date there are only a handful of quantum algorithms [Williams and Clearwater, 1997].
Research in quantum technology has shown that quantum computers can provide dramatic advantages over classical computers for some problems. The efficiency of quantum computing is considered to become so significant that the study of quantum algorithms has attracted widespread interest. Development of quantum algorithms and circuits is difficult for a human researcher, so automatic induction of computer programs by means of genetic programming, which uses almost no auxiliary information on the search space, proved to be useful in generating new quantum algorithms. This approach takes advantage of the intrinsic parallelism of the genetic algorithm and quantum computing parallelism. The paper begins with a brief review on some basic concepts in genetic algorithms and quantum computation. Next, it describes an application of genetic programming for evolving quantum computing circuits.
Research in quantum technology has shown that quantum computers can provide dramatic advantages over classical computers for some problems. The efficiency of quantum computing is considered to become so significant that the study of quantum algorithms has attracted widespread interest. Development of quantum algorithms and circuits is difficult for a human researcher, so automatic induction of computer programs by means of genetic programming, which uses almost no auxiliary information on the search space, proved to be useful in generating new quantum algorithms. This approach takes advantage of the intrinsic parallelism of the genetic algorithm and quantum computing parallelism. The paper begins with a brief review on some basic concepts in genetic algorithms and quantum computation. Next, it describes an application of genetic programming for evolving quantum computing circuits.
Genetic algorithms are widely used for solving optimization problems. As the search space of the problem is augmented, the probability of falling into local minima will be augmented as well. Previous works showed that the adjusted phase (i.e. required modification per generation), may enhance badly the optimality reaching. Moreover, the search for suitable probabilities on crossover and mutation results in huge computational complexity. Recent developments in quantum technology have shown that quantum computers can provide a dramatic advantage over classical computers. This paper will be oriented toward computer scientists interested in the quantum genetic algorithm (QGA). Therefore, we will present a discussion, of the pros, and cons of classical GA. We survey the “state of the art” of the most known techniques for evolving evolutionary algorithms and quantum computing. We intend in this work to help researchers and application developers in gaining insights into the power of quantum computing mechanisms. This science can inherently treat the balance between exploration and exploitation of the search space. This property is due to the use of Q-bits. The orientation toward the best solution is due to the use of Q-gates.
Recently, researchers have applied genetic algorithms (GAs) to address some problems in quantum computation. Also, there has been some works in the designing of genetic algorithms based on quantum theoretical concepts and techniques. The so called Quantum Evolutionary Programming has two major sub-areas: Quantum Inspired Genetic Algorithms (QIGAs) and Quantum Genetic Algorithms (QGAs). The former adopts qubit chromosomes as representations and employs quantum gates for the search of the best solution. The later tries to solve a key question in this field: what GAs will look like as an implementation on quantum hardware? As we shall see, there is not a complete answer for this question. An important point for QGAs is to build a quantum algorithm that takes advantage of both the GA and quantum computing parallelism as well as true randomness provided by quantum computers. In the first part of this paper we present a survey of the main works in GAs plus quantum computing including also our works in this area. Henceforth, we review some basic concepts in quantum computation and GAs and emphasize their inherent parallelism. Next, we review the application of GAs for learning quantum operators and circuit design. Then, quantum evolutionary programming is considered. Finally, we present our current research in this field and some perspectives.
Recent development in quantum technology have shown that quantum computer can provide a dramatic advantage over classical computers for some algorithms. In particular, a polynomial-time algorithm for factoring, a problem which was previously thought to be hard for classical computers, has recently been developed [13]. Similarly, a quantum algorithm searching for unsorted database in square root of time it would take on a classical computer has also been described by Grover [4] - [3]. Both algorithms rely upon the inherent parallelism, superposition and entanglement property of quantum computing to achieve their improvements. Since most problems of real interest for genetic algorithms have a vast search space, it seems appropriate how quantum parallelism can be applied to Genetic Algorithms. In this paper we provide a brief background of quantum computers. We explain why and how quantum algorithms provides a fundamental improvements over classical ones for some problems. Further, we present here the Conventional Genetic Algorithm and the quantum approach of Genetical Algorithms(QGA) as well. The benefits and drawbacks of QGA are also analyzed. Moreover, this paper provides an improved version over the conventional QGA. This improvement originates from the best partial immigration technique applied to the quantum chromosomes. The main objective of the best partial immigration is to consider the string of qubits from the quantum chromosomes having best fitness and transfer the same randomly to the chromosomes of next generation for better mixing. The process is reiterated. To observe the performance the best partial immigration technique we have considered some popular optimization problems and performed the experiment on it. These problems are namely Travelling Salesman Problem(TSP), Binpacking Problem and Vertex Cover Problem. It has been observed that the obtained results outperforms the conventional QGA.