Quantum artificial intelligence (QAI) is a topic of study that blends quantum computing and AI. In order to investigate the possibilities of fruitful cooperation across both disciplines of computer science (CS), the authors will attempt to convey some of the relevant properties of both artificial intelligence (AI) and quantum computing (QC) from an academic point of view. The key artificial intelligence applications that can be addressed more effectively by employing the current generation of quantum computers will be covered in this chapter, along with a review of the fundamental components of quantum computing. There are several similarities between quantum computing and artificial intelligence. Quantum computing can increase the training speed and processing capability of artificial intelligence and machine learning algorithms at a lower cost. This chapter discusses the fundamental ideas that enable scientists and engineers to combine AI with quantum computing.
Top1. Introduction
A great technology that has developed over time, quantum computing is 100 trillion times faster than a supercomputer. It is based on quantum theory, which has the ability to perform computations utilizing both linear algebra and probability theory. The concept of Quantum Computation (QC) was originally presented in 1982 by Richard Feynman the scientific discipline of quantum computation (QC) investigates how the quantum behavior of specific subatomic particles (such as photons, electrons, etc.) might be used to carry out computation and eventually massive amounts of information processing.
Two crucial quantum phenomena, superposition and entanglement, offer a considerably more effective approach to carry out specific types of computations than traditional algorithmic techniques. Information is kept in quantum registers, which are made up of a series of quantum bits in QC. To conduct straightforward qubit-range operations on quantum registers, QC defines a collection of operators called quantum gates. Computations. Quantum algorithms are applications of numerous quantum techniques in succession do more complex computations using gates on a quantum register as shown in Figure 1 (Gyongyosi et al., 2021).
The field of artificial intelligence (AI) and quantum computing are combined in the ground-breaking technology known as quantum artificial intelligence (QAI). By facilitating quicker and more precise problem-solving, forecasts, and optimizations, this innovative technique has the potential to completely transform the field of artificial intelligence. The whole field of information processing will benefit greatly from QC's capacity to carry out parallel information processing and quick searches over unordered sets of data. The advantages of QC in the field of artificial intelligence (AI) are the main topic of this essay. The current essay makes an effort to take a more comprehensive approach to quantum approaches for AI applications, taking into account both past research and some big-picture concepts for future study as shown in Figure 2 (Mangini et al., 2021).
Figure 2. Artificial intelligence applications
But Due to space constraints, it first provides a very quick overview of the fundamentals of quality control Algorithms, simply the basics to grasp the topic. Since several successful applications of AI have demonstrated that we can rely on quantum computers for calculations, interest in using them to compute artificially intelligent algorithms has grown tremendously. Section 1 of this study will explain the foundations of quantum computing from the standpoint of the quantum computing programming paradigm (Wichert et al., 2020).
Understanding quantum computing, QAI's underlying technology, is crucial for a complete understanding. Quantum computing uses qubits, which can be in several states at once, as opposed to classical computing, which processes data using binary bits that can either be 0 or 1.
Due to this special characteristic, quantum computers are able to process complex calculations significantly faster than conventional computers. QAI has the potential to revolutionize our approach to computing and problem-solving by fusing the enormous computational power of quantum computing with the suppleness and adaptability of AI.
According to reports, quantum computing can support artificial intelligence in a variety of ways, including the processing of enormous complicated datasets and the evolution of algorithms to facilitate improved learning, reasoning, and comprehension. Quantum computing opens up a number of opportunities for machine learning algorithms and natural language processing. For instance, a “meaning aware” algorithm was recently created by running a natural language processing algorithm on quantum computing (Dyakonov et al., 2019).