Contemporarily, with the slow-down development speed of classical computing, quantum computing is becoming the focus of research as a replacing technique. It is a well-known approach that can offer exponential speed-up for a certain type of calculation issues based on the state-of-art optical facilities and techniques. In this paper, the development of quantum computing will be briefly introduced firstly. Subsequently, this paper will demonstrate the principle of different types of quantum algorithms as well as the realization scenarios. Afterward, the similarities, as well as differences between the conventional algorithms and the quantum algorithms in the genetic algorithm and ant colony algorithm, will be compared and analyzed. Based on the analysis, it is obvious that quantum algorithms are more powerful in solving specific problems compared with conventional algorithms, where the speed is much quicker than the traditional approaches. According to the results, it’s necessary to study the practical application of quantum algorithms. These results shed light on guiding further exploration of quantum algorithms.
Contemporarily, with the slow-down development speed of classical computing, quantum computing is becoming the focus of research as a replacing technique. It is a well-known approach that can offer exponential speed-up for a certain type of calculation issues based on the state-of-art optical facilities and techniques. In this paper, the development of quantum computing will be briefly introduced firstly. Subsequently, this paper will demonstrate the principle of different types of quantum algorithms as well as the realization scenarios. Afterward, the similarities, as well as differences between the conventional algorithms and the quantum algorithms in the genetic algorithm and ant colony algorithm, will be compared and analyzed. Based on the analysis, it is obvious that quantum algorithms are more powerful in solving specific problems compared with conventional algorithms, where the speed is much quicker than the traditional approaches. According to the results, it’s necessary to study the practical application of quantum algorithms. These results shed light on guiding further exploration of quantum algorithms.
Contemporarily, whether quantum computing performs better than conventional computers remain an unresolved issue. In this paper, we compare the time complexity between quantum and conventional computers for a specific type of issue. Theoretically, quantum computation is better in solving nonlinear calculations, the quantitative evaluation of the computing ability should be investigated. In this paper, the Fourier transform was applied in both classical and quantum logical circuits to calculate the theoretical time complexities respectively and make a comparison after that. According to the analysis, the ideal quantum computer performed really fast as the number of tasks increased, but the gate-level accurate quantum circuit, which was a simulated circuit was running slower. The paper shows that the current technology cannot perform the perfect quantum computer, but the future of it would be very promising. Overall, these results shed light on guiding further exploration of quantum computing.
Contemporarily, the classical computing has met the upper limitations of Moore’s law due to the restrictions of the resolution for light sourcing and material. On this basis, the quantum computing has been rapidly developed due to the advantages of parallel computation, which is able to be faster more than 3 orders of magnitude than conventional computer in some issues. With this in mind, this study discusses the basic principles of quantum computing as well as the corresponding state-of-art applications. To be specific, the developing history of the quantum computing is briefly introduced. Subsequently, the entanglement principle will be demonstrated accordingly. Afterwards, three types of the state-of-art quantum computing facilities will be demonstrated. Then, some of the applications and common algorithms will be discussed simultaneously. According to the analysis, the limitations and defects of current investigation of quantum computing will be illustrated. In conclusion, these outcomes provided guideline for further exploration of quantum computing.
Quantum computers are capable of ultra fast computation in the fields where classical computers fail. Even though quantum computers are nowhere near commercialization, many researchers have developed quantum algorithms in fields such as modern encryption and molecular simulation, which, in theory, are exponentially faster than their classical counterparts. In this case, this paper will discuss the advantages of quantum computers over classical computers in those fields by examining and analyzing the various quantum algorithms. To be specific, the develop routine as well as detail examples will be exhibited to illustrate the differences and preferences. In addition, this study will also fully aware of the challenges that quantum computing researchers are facing. On this basis, possible limitations of quantum computers are also presented. The aim is to promote interest in quantum computing by introducing their supremacy in modern encryption and biological science. These results shed light on guiding further exploration of quantum computing algorithms.
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.
Quantum computing is a rapidly growing field. Quantum algorithms play a significant role and have applications in cryptography, machine learning, optimization, and computational issues [1][2]. They quantum features like entanglement and superposition that help carryout tasks that would be difficult for a classical computer. More Interest has been developed in Quantum algorithms due to their ability to solve specific computational problems quicker than that in classical algorithms [3]. These algorithms use fundamentals of quantum physics to carryout calculations quickly and effectively than traditional algorithms. Here in-depth analysis of quantum algorithms and classical algorithms is done by looking at the time and space complexity of quantum and classical algorithms including their capacity to tackle specific types of issues.First, we conduct a general comparison of Classical and Quantum Algorithm along with Background and motivation. We also discuss Simon's algorithm first and then Grover's algorithm in depth and their comparison with few other algorithms in a table format [5][6][7][8]. We also cover some recent advancements, Quantum supremacy, Post-Quantum Cryptography, 8 Quantum Artificial Intelligence, Hybrid Quantum-Classical Algorithms and few other important parameters. We also discuss the plausible impact of quantum algorithms on cybersecurity and cryptography [13][9]. Overall, this comparative study shows the uses and challenges involved in implementing quantum algorithms while offering an overview of quantum algorithms. We conclude by the fundamental ideas of quantum algorithms. Adding to this we also discuss the challenges and limitations of using quantum algorithms in practically. At the end, we provide a summary of the primary findings from this comparative study of Quantum and classical algorithms.
Quantum ant colony optimization (QACO) has drew much attention since it combines the advantages of quantum computing and ant colony optimization (ACO) algorithms and overcomes some limitations of the traditional ACO algorithm. However, due to the hardware resource limitations of currently available quantum computers, such as the limited number of qubits, lack of high-fidelity gating operation, and low noisy tolerance, the practical application of the QACO is quite challenging. In this paper, we introduce a hybrid quantum-classical algorithm by combining the clustering algorithm with QACO algorithm, so that this extended QACO can handle large-scale optimization problems, which makes the practical application of QACO based on available quantum computation resource possible. To verify the effectiveness and performance of the algorithm, we tested the developed QACO algorithm with the Travelling Salesman Problem (TSP) as benchmarks. The developed QACO algorithm shows better performance under multiple data set. In addition, the developed QACO algorithm also manifests the robustness to noise of calculation process, which is typically a major barrier for practical application of quantum computers. Our work shows that the combination of the clustering algorithm with QACO has effectively extended the application scenario of QACO in current NISQ era of quantum computing.