摘要 :
There exist quantum algorithms that are more efficient than their classical counterparts; such algorithms were invented by Shor in 1994 and then Grover in 1996. A lack of invention since Grover's algorithm has been commonly attrib...
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There exist quantum algorithms that are more efficient than their classical counterparts; such algorithms were invented by Shor in 1994 and then Grover in 1996. A lack of invention since Grover's algorithm has been commonly attributed to the non-intuitive nature of quantum algorithms to the classically trained person. Thus, the idea of using computers to automatically generate quantum algorithms based on an evolutionary model emerged. A limitation of this approach is that quantum computers do not yet exist and quantum simulation on a classical machine has an exponential order overhead. Nevertheless, early research into evolving quantum algorithms has shown promise. This paper provides an introduction into quantum and evolutionary algorithms for the computer scientist not familiar with these fields. The exciting field of using evolutionary algorithms to evolve quantum algorithms is then reviewed.
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Evolving solutions rather than computing them certainly represents a promising programming approach. Evolutionary computation has already been known in computer science since more than 4 decades. More recently, another alternative...
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Evolving solutions rather than computing them certainly represents a promising programming approach. Evolutionary computation has already been known in computer science since more than 4 decades. More recently, another alternative of evolutionary algorithms was invented: Quantum Genetic Algorithms (QGA). In this paper, we outline the approach of QGA by giving a comparison with Conventional Genetic Algorithm (CGA). Our results have shown that QGA can be a very promising tool for exploring search spaces.
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The selection mechanism of genetic algorithms can play a key role in leading the optimization process towards suitable solutions of a given problem, as their application can affect the trade-off between exploration and exploitatio...
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The selection mechanism of genetic algorithms can play a key role in leading the optimization process towards suitable solutions of a given problem, as their application can affect the trade-off between exploration and exploitation. However, some selection operators suffer from an excessive exploitation that may lead to the loss of population diversity and to a premature convergence, and others present an excessive exploration that may cause genetic algorithms to produce poor results near to random. As a consequence, there is a strong need to rethink how a mating pool is created and introduce alternative approaches to genetic selection. In this paper, a quantum algorithm, named Quantum Genetic Sampling (QGS), has been designed to replace selection operators so as to provide an increased population diversity in genetic evolution and reduce the possibility that the optimization process converge to low quality solutions. The proposed approach is based on two sequential steps. In the first step, QGS models a problem's search space through a quantum state, where each quantum basis state is related to a possible solution of the problem. In the second step, QGS uses quantum amplitude amplification to properly adjust the probability of measuring an individual and placing it in the mating pool according to its fitness value. Unlike conventional selection operators, the quantum mechanical nature of the proposed approach allows for a non-zero probability of introducing new genetic material into the mating pool. Experiments based on well-known benchmark functions show the suitability of the proposed quantum operator for embedding in genetic algorithms, both in terms of accuracy and population diversity.
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Optimization is one of the research areas where quantum computing could bring significant benefits. In this scenario, a hybrid quantum-classical variational algorithm, the Quantum Approximate Optimization Algorithm (QAOA), is rece...
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Optimization is one of the research areas where quantum computing could bring significant benefits. In this scenario, a hybrid quantum-classical variational algorithm, the Quantum Approximate Optimization Algorithm (QAOA), is receiving much attention for its potential to efficiently solve combinatorial optimization problems. This approach works by using a classical optimizer to identify appropriate parameters of a problem-dependent quantum circuit, which ultimately performs the optimization process. Unfortunately, learning the most appropriate QAOA circuit parameters is a complex task that is affected by several issues, such as search landscapes characterized by many local optima. Moreover, gradient-based optimizers, which have been pioneered in this context, tend to waste quantum computing resources. Therefore, gradient-free approaches are emerging as promising methods to address this parameter-setting task. Following this trend, this paper proposes, for the first time, the use of genetic algorithms as gradient-free methods for optimizing the QAOA circuit. The proposed evolutionary approach has been evaluated in solving the MaxCut problem for graphs with 5 to 9 nodes on a noisy quantum device. As the results show, the proposed genetic algorithm statistically outperforms the state-of-the-art gradient-free optimizers by achieving solutions with a better approximation ratio.
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This paper introduces a new evolutionary algorithm with the support of an actual quantum processor, a computing device which uses phenomena from quantum mechanics to enable a considerable speed-up in computation. In particular, th...
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This paper introduces a new evolutionary algorithm with the support of an actual quantum processor, a computing device which uses phenomena from quantum mechanics to enable a considerable speed-up in computation. In particular, the proposed approach uses quantum superposition and entanglement to implement quantum evolutionary concepts such as quantum chromosome, entangled crossover, rotation mutation, and quantum elitism, to efficiently perform genetic evolution on quantum devices, and converge towards proper sub-optimal solutions of a given optimization problem. The proposed quantum genetic algorithm has been implemented by using a hybrid hardware architecture, where classical processors interact with the family of quantum processors provided by the IBM Q Experience (R) initiative. As shown in the experimental section, the proposed quantum genetic algorithm's performance highlights that the synergy between quantum and evolutionary computation results in a new and promising bio-inspired optimization strategy. (C) 2021 Elsevier Inc. All rights reserved.
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We present a quantum BP neural network with the universality of single-qubit rotation gate and two-qubit Controlled-NOT gate. Also, we show the process of the BP learning algorithm for the quantum model, and propose an improved BP...
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We present a quantum BP neural network with the universality of single-qubit rotation gate and two-qubit Controlled-NOT gate. Also, we show the process of the BP learning algorithm for the quantum model, and propose an improved BP learning algorithm based on quantum genetic algorithm. The type recognition simulation of the Matlab program shows the efficiencies of the quantum neural network and the improved learning algorithm.
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Quantum genetic coding method plays an important role in improving the efficiency of optimization algorithm. The existing quantum genetic algorithm has some defects, that quantum encoding scheme is tend to reduce the stability, so...
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Quantum genetic coding method plays an important role in improving the efficiency of optimization algorithm. The existing quantum genetic algorithm has some defects, that quantum encoding scheme is tend to reduce the stability, so that the algorithm is prone to premature convergence and falls into local minima. Therefore, the chain of multiple genes encoding scheme is used to extend in the multi-dimensional space for improving this algorithm. By function extremum and simulation of neural network weights optimization, according to the characteristics of qubits and the normalization condition, double and triple chain binding coding schemes are proposed. By experiments on multiple genes encoding scheme chain, the performance of the algorithm is tested. It shows that the algorithm can get better results by increasing the higher accuracy of solution chain genes. It is an effective strategy to improve the performance of genetic coding.
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Testing of VLSI circuits is still a NP hard problem. Existing conventional methods are unable to achieve the required breakthrough in terms of complexity, time and cost. This paper deals with testing the VLSI circuits using natura...
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Testing of VLSI circuits is still a NP hard problem. Existing conventional methods are unable to achieve the required breakthrough in terms of complexity, time and cost. This paper deals with testing the VLSI circuits using natural computing methods. Two prototypical algorithms named as DATPG and QATPG are developed utilizing the properties of DNA computing and Quantum computing, respectively. The effectiveness of these algorithms in terms of result quality, CPU requirements, fault detection and number of iterations is experimentally compared with some of existing classical approaches like exhaustive search and Genetic algorithms, etc. The algorithms developed are so efficient that they require only √N (where N is the total number of vectors) iterations to find the desired test vector whereas in classical computing, it takes N/2 iterations. The extendibility of new approach enables users to easily find out the test vector from VLSI circuits and can be adept for testing the VLSI chips.
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Hashing is a widely used technique in computer science. The recently proposed quantum hashing has also proved its usefulness in a number of applications. The key property of both classical and quantum hashing is the ability to wit...
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Hashing is a widely used technique in computer science. The recently proposed quantum hashing has also proved its usefulness in a number of applications. The key property of both classical and quantum hashing is the ability to withstand collisions however, the notion of collision itself is different in the classical and quantum setting. In this study we analyze the set of numeric parameters that determine the probability of quantum collisions for the quantum hashing. Although, there is a general method of obtaining good hashing parameters, it makes sense for comparatively large inputs. That is why we construct different methods to complement the general one. We present two explicit optimization algorithms for computation of quantum hashing parameters: one is based on the genetic approach and the other uses the annealing simulation. The solution to the considered optimization problem can be used for the variety of quantum hash functions and also provides a solution to the general problem of constructing sets of pairwise distinguishable states in low-dimensional spaces.
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This letter proposes two algorithms: a novel Quantum Genetic Algorithm (QGA) based on the improvement of Han's Genetic Quantum Algorithm (GQA) and a new Blind Source Separation (BSS) method based on QGA and Independent Component A...
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This letter proposes two algorithms: a novel Quantum Genetic Algorithm (QGA) based on the improvement of Han's Genetic Quantum Algorithm (GQA) and a new Blind Source Separation (BSS) method based on QGA and Independent Component Analysis (ICA). The simulation result shows that the efficiency of the new BSS method is obviously higher than that of the Conventional Genetic Algorithm (CGA).
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