Idea Of Quantum Computation Research Articles

Validity in evaluation: where is the argument-based approach heading?

The evolution of the concept of validity is examined in the context of the integration of Generative Artificial Intelligence and ethical stances, and with it, informed decision-making. The methodology used includes the history of concepts as laid out by Koselleck, analyzing how the concept of validity is a fundamental concept. The method used is a literature review, analyzing historical and contemporary perspectives and arguments from influential authors such as Messick and Kane. This conceptual journey leads us to recognize that validity is not a monolithic entity, but a complex fabric of multiple theoretical and practical threads, ranging from the internal logic of evaluations to the repercussions of their application in society. Furthermore, validity is recognized as a complex construct that cannot be simplified to a single aspect or characteristic of a test or evaluation, differentiating between validity and validation. The five historical periods distinguished in the literature that reflect paradigmatic changes in the understanding of validity were: gestational, crystallization, fragmentation, reunification, deconstruction, culminating with the period of diffusion. The most relevant conclusion is that validity is not static but dynamic, evolving with context and application. It also emphasizes the need for continuous validation adapted to emerging challenges, such as Generative Artificial Intelligence (GenAI), with the goal of ensuring that evaluations are accurate and fair amid a growing trend on ideas of quantum computing.

A Brief Study on Quantum Computing

In the twentieth century, quantum physics is one of the most successful approaches in scientific progress. To theorize the concept of quantum computing Max Planck's idea of the existence of energy in individual units like matter was used. Since then, the idea of manufacturing quantum computers for everyday use is becoming more plausible with technological advances in quantum theory. A classical computer carries out logical operations using a bit which is either 1 or 0. In contrast to a classical computer, a quantum computer works with a quantum bit or qubit, which is not limited to two states only. Qubits can exist in a superposition of the two states creating parallelism. Qubit represents the state of atoms, ions, photons or electrons. These qubits which work together with a control device act as a computer memory.Quantum computers have the potential to become million times more potent than present-day supercomputers due to the presence of these multiple states simultaneously. Concepts and ideas of quantum computing have been demonstrated by different methods like Ion Trap, Quantum Dot methods etc. However, the actual realization of such a superior system is still in the future. A strategy must be formulated to maintain decoherence and other potential sources of error at a permissible level.Astonishingly, quantum computing took a long time to take off, even though the physicists have known about the world of subatomic particles. Even so, it took computer scientists another half-century to consider harnessing quantum effects for computation. Also, it was later discovered that quantum computers could solve quantum mechanical many-body problems that are impractical to solve on a classical computer. The foundations of the subject of quantum computation may have become well-published, but the knowledge is still growing. Hence here is a research paper based on the study of quantum computation.

Prediction of RNA Secondary Structure Using Quantum-inspired Genetic Algorithms

Background: The prediction of RNA secondary structure using optimization algorithms is key to understand the real structure of an RNA. Evolutionary algorithms (EAs) are popular strategies for RNA secondary structure prediction. However, compared to most state-of-the-art software based on DPAs, the performances of EAs are a bit far from satisfactory. Objective: Therefore, a more powerful strategy is required to improve the performances of EAs when applied to the prediciton of RNA secondary structures. Methods: The idea of quantum computing is introduced here yielding a new strategy to find all possible legal paired-bases with the constraint of minimum free energy. The sate of a stem pool with size N is encoded as a population of QGA, which is represented by N quantum bits but not classical bits. The updating of populations is accomplished by so-called quantum crossover operations, quantum mutation operations and quantum rotation operations. Results: The numerical results show that the performances of traditional EAs are significantly improved by using QGA with regard to not only prediction accuracy and sensitivity but also complexity. Moreover, for RNA sequences with middle-short length, QGA even improves the state-of-art software based on DPAs in terms of both prediction accuracy and sensitivity. Conclusion: This work sheds an interesting light on the applications of quantum computing on RNA structure prediction.

A short survey of quantum computing

Quantum computing is closely related to [Formula: see text]-dimensional topological quantum field theory through its approach in topological quantum computation. The following are notes based on lectures given by the author at the Mathematics Department of George Washington University. The idea is to give a brief introduction and survey of a few basic facts of quantum computing to an audience of non-experts interested in taking a quick look at some of the most important concepts of quantum computing. The author is a topologist with strong interest in the general ideas of quantum computing. The notes touch on how quantum topology enters the field but emphasizes the general ideas of quantum computing in the first place.

Improved Quantum Particle Swarm Optimization for Mangroves Classification

Quantum particle swarm optimization (QPSO) is a population based optimization algorithm inspired by social behavior of bird flocking which combines the ideas of quantum computing. For many optimization problems, traditional QPSO algorithm can produce high-quality solution within a reasonable computation time and relatively stable convergence characteristics. But QPSO algorithm also showed some unsatisfactory issues in practical applications, such as premature convergence and poor ability in global optimization. To solve these problems, an improved quantum particle swarm optimization algorithm is proposed and implemented in this paper. There are three main works in this paper. Firstly, an improved QPSO algorithm is introduced which can enhance decision making ability of the model. Secondly, we introduce synergetic neural network model to mangroves classification for the first time which can better handle fuzzy matching of remote sensing image. Finally, the improved QPSO algorithm is used to realize the optimization of network parameter. The experiments on mangroves classification showed that the improved algorithm has more powerful global exploration ability and faster convergence speed.

Teaching Quantum Computing with the QuIDE Simulator

Recently, the idea of quantum computation is becoming more and more popular and there are many attempts to build quantum computers. Therefore, there is a need to introduce this topic to regular students of computer science and engineering. In this paper we present a concept of a course powered by the Quantum Integrated Development Environment (QuIDE), the new quantum computer simulator that joins features of GUI based simulators with inter- preters and simulation library approach. The idea of the course is to put together theoretical aspects with practical assignments realized on the QuIDE simulator. Such an approach enables studying a variety of topics in a way understandable for this category of students. The topics of the course included understanding the concept of quantum gates, registers and a series of algorithms: Deutsch and Bernstein-Vazirani Problems, Grover's Fast Database Search, Shor's Prime Factorization, Quantum Teleportation and Quantum Dense Coding. We describe results of QuIDE assessment during the course; our solution scored more points in System Usability Scale survey then the other tool previously used for that purpose. We also show that the most useful features of such a tool indicated by students are similar to the assumptions made on the simulator functionality.

Spin in CdTe/ZnTe Quantum Dot: Its Potential for Information Storage

Inventions of transistor [1] and integrated circuit [2] established a close link between semiconductor physics and information processing. Rapid development of the field, aptly described by Moore’s law [3], based on optimization of growth and processing techniques as well as on progress in designing complex CPU circuits. Quantum phenomena, inevitable in nanoscale systems, have been considered a limitation to further development of classical electronics according to Moore’s law. Simultaneously, quantum mechanics was recognized as an opportunity to extend the bounds of classical computational paradigm through an idea of quantum computer, described by Deutsch in 1985 [4]. Physical realization of proper building blocks for quantum computer — qubits conform to certain requirements [5] — exhibits major experimental difficulty. Many different approaches were proposed, including trapped ions [6], cavity QED systems [7], liquid state NMR [8], gated quantum dots [9], superconductors [10], crystal lattice impurities [11], and others. Among all these possible solutions, solid-state systems are particularly promising due to relative ease of qubit manipulation, e.g., by electric, optical or magnetic fields. Prospects of future integration of qubits with existing classical electronics also constitute a significant advantage. Drawbacks of the solid state-based systems are related mainly to short coherence times limited by the interaction with the environment. The interaction with the surrounding crystal matrix may be suppressed by e.g. constraining otherwise mobile carriers inside potential well of a quantum dot. This paper presents a selection of our studies of spins in self-assembled CdTe/ZnTe quantum dots from the point of view of quantum information processing. We will discuss three basic processes related to a single quantum dot used as a qubit, that is read-out of encoded information, writing (initialization) the qubit state and evolution of the qubit state which provides means to manipulate it. 2. Samples

Isotopically engineered silicon/silicon-germanium nanostructures asbasic elements for a nuclear spin quantum computer

The idea of quantum computation is the most promising recent development inthe high-tech domain, while experimental realization of a quantum computerposes a formidable challenge. Among the proposed models especially attractiveare semiconductor based nuclear spin quantum computers (S-NSQCs), wherenuclear spins are used as quantum bistable elements, `qubits', coupled tothe electron spin and orbital dynamics. We propose here a scheme forimplementation of basic elements for S-NSQCs which are realizable withinachievements of the modern nanotechnology. These elements are expected to bebased on a nuclear-spin-controlled isotopically engineered Si/SiGeheterojunction, because in these semiconductors one can vary the abundance ofnuclear spins by engineering the isotopic composition. A specific device issuggested, which allows one to model the processes of recording, reading andinformation transfer on a quantum level using the technique of electricaldetection of the magnetic state of nuclear spins. Improvement of thistechnique for a semiconductor system with a relatively small number of nucleimight be applied to the manipulation of nuclear spin `qubits' in the futureS-NSQCs.