Beginning with the concern of simulation of nonclassical physical theories (deal with tiny particles) that share one common feature (quantum mechanics). As well as, Shor's algorithm has given the advantage of new technology. moreover, Moore's law predicts the technology that is based on classical physics could be reached its limitations; finally, the efficiency of the performance of implementing the applications in this innovation, In particular, the drug processing that affects the human life especially in these days with coronavirus pandemic, all these facts pushed the technology to produce, develop, and use the quantum computer in various field of life, this paper discusses this fact and get the conclusion of advantage and disadvantage of this emerging technology.
Beginning with the concern of simulation of nonclassical physical theories (deal with tiny particles) that share one common feature (quantum mechanics). As well as, Shor's algorithm has given the advantage of new technology. moreover, Moore's law predicts the technology that is based on classical physics could be reached its limitations; finally, the efficiency of the performance of implementing the applications in this innovation, In particular, the drug processing that affects the human life especially in these days with coronavirus pandemic, all these facts pushed the technology to produce, develop, and use the quantum computer in various field of life, this paper discusses this fact and get the conclusion of advantage and disadvantage of this emerging technology.
As technological innovations in computers begin to advance past their limit (Moore’s law), a new problem arises: What computational device would emerge after the classical supercomputers reach their physical limitations? At this moment in time, quantum computers are at their starting stage and there are already some strengths and advantages when compared with modern, classical computers. In its testing period, there are a variety of ways to create a quantum computer by processes such as the trapped-ion and the spin-dot methods. Nowadays, there are many drawbacks with quantum computers such as issues with decoherence and scalability, but many of these issues are easily emended. Nevertheless, the benefits of quantum computers at the moment outweigh the potential drawbacks. These benefits include its use of many properties of quantum mechanics such as quantum superposition, entanglement, and parallelism. Using these basic properties of quantum mechanics, quantum computers are capable of achieving faster computational times for certain problems such as finding prime factors of an integer by using Shor’s algorithm. From the advantages such as faster computing times in certain situations and higher computing powers than classical computers, quantum computers have a high probability to be the future of computing after classical computers hit their peak.
Quantum computing is an emerging technology. The clock frequency of current computer processor systems may reach about 40 GHz within the next 10 years. By then, one atom may represent one bit. Electrons under such conditions are no longer described by classical physics and a new model of the computer may be necessary by then. The quantum computer is one proposal that may have merit in dealing with the problems associated with the fact that certain important computationally intense problems present that current (classical) computers cannot solve because they require too much processing time. For example, Shor's algorithm performs factoring a large integer in polynomial time while classical factoring algorithms can do it in exponential time. In this paper we briefly survey the current status of quantum computers, quantum computer systems, and quantum simulators. Keywords Classical computers, quantum computers, quantum computer systems, quantum simulators, Shor's algorithm.
Since last years, computers reduce the human efforts and give improvement in its performance day by day.Quantum computers are those computers who perform computing which is based on the phenomenon called Quantum Mechanics.It is a fusion of classical computing which we were using now days, physics and mathematics.And the computing they performed by integrating these fields is termed as Quantum Computing.Quantum computers perform their computing on the basis of the microscopic particles like ions, photons, electrons, protons, neutrons etc which ultimately give exponentially high computation power and other advantages too like less consumption of electricity which we will discuss in the same paper in brief over the classical computers, which we were using till day.In this paper we will briefly explain the quantum computers, its history, its properties in which its computing is based upon, its advantages& disadvantages, its limitation, its real world application, the difference between classical computers and Quantum computers and the challenges it faced and at last we conclude it with its future scope.
Quantum computing is an emergent technology with great promise to reshape machine learning. It enables computations intractable by any classical system. This paper discusses the basic concepts of quantum computing, its advantages, and their limitations regarding ML. It can be concluded that quantum computers leverage phenomena in quantum mechanics, such as superposition, entanglement, and interference execute those computations that are beyond the capability of classical systems. A few of the most important fancies in QSVM, QPCA, and QNN perform such tasks as computing kernel matrices, high-dimensional data processing, and solving optimization problems in some key points. In contrast, several formidable challenges persist, beginning with the scalability and coherence of qubits as hardware-related issues, and extending to software-centric problems such as data encoding and the efficiency of specific algorithmic applications. This paper also underscores potential future avenues for advancement, including the development of hybrid quantum-classical algorithms, quantum error correction techniques, and quantum-native machine learning models, aimed at surmounting these limitations. By addressing these challenges and directing focus towards prospective research, quantum computing holds the promise of unlocking transformative breakthroughs in various domains such as big data processing, drug development, financial modeling, and natural language processing. Such advancements could firmly establish quantum computing as the cornerstone of next-generation artificial intelligence technologies.
Quantum computing has become a hot topic in recent years. It provides high consumption and exponential speed over classical computers by controlling the behaviour of small physical objects i.e., microscopic particles like atoms, electrons, photons, etc. Quantum computing is an emerging technology. The clock frequency of current computer processor systems may reach about 40 GHz within the next 10 years. By then, one atom may represent one bit. Electrons under such conditions are no longer described by classical physics, and a new model of the computer may be necessary by that time. The quantum computer is one proposal that may have merit in dealing with the problems presented. Currently, there exist some algorithms utilizing the advantage of quantum computers. For example, Shor's algorithm performs factoring of a large integer in polynomial time, whereas classical factoring algorithms can do it in exponential time. In this paper, we will briefly explain quantum computers, their history, the difference between classical computers and Quantum computers, and the challenges they faced Lastly, we conclude with its future scope.
Quantum machine learning (QML) has been demonstrated to have great potential in revolutionizing the process of drug discovery by dealing with the problem of high computational complexity of molecular simulations. Conventional computational techniques are not very effective in predicting the properties of molecules and pharmaceutical activity because the simulation of large molecules is too complex. This paper will discuss how QML algorithms can be used, specifically variational quantum circuits and quantum kernel methods, to improve the predictability of molecular properties and accelerate the process of drug discovery. To enhance the prediction accuracy, the research use quantum-enhanced feature extraction together with classical machine learning technologies. Research methodology is applied to datasets of molecular structure and biological activity in order to provide a more useful model to learn about how molecular features relate to biological outcomes using quantum machine learning models. These results suggest that it is 15 % more accurate in prediction and 20 % faster in computing high-dimensional molecular simulations compared to standard machine learning algorithms. The efficiency of the QML-based method is statistically proven with an accuracy 89%, precision 87%, and an F1-score of 0.91. The results point to the possible ability of QML to decrease the time and computational resources spent on the exploration of drug discovery, providing a new direction for the improvement of molecular property prediction. The study is a contribution to the emerging area of QML in pharmaceutical use and demonstrates that it can simplify drug discovery and enhance the accuracy with which predictions are made. The paper demonstrates that QML may produce a significant impact on the drug development process by simulating the molecular properties faster, more effectively, and more accurately.