In the ever-changing technological environment, the combination of artificial intelligence (AI) with quantum computing represents an enormous potential frontier. This collaboration has the potential to transform industry, scientific research, and problem-solving approaches. At its heart, AI's function in the quantum computing age includes several transformational components. AI has infiltrated many facets of our everyday lives, from smartphone technology to autonomous car features and novel shopping experiences. Its seamless integration has recently become a major fixture, especially with the emergence of generative AI. Surprisingly, generative AI is set to take quantum computing beyond mere dreams and into the realm of quantum supremacy, where quantum computers vastly outperform traditional approaches. Combining AI and quantum computing might expedite and refine the latter's already impressive skills in addressing complex issues. Such a collaboration might elevate AI from outstanding to game changing.
Top1. Introduction
Quantum computing is defined as computing based on quantum physics. Data is traditionally represented as bits that can be either 1 or 0. Because of the feature of superposition, qubits in quantum computing can be both 1 and 0. It is an area of research that performs computer tasks using quantum mechanics ideas. It makes use of quantum bits, or qubits, which may be in several states at the same time, as opposed to classical bits, which can only be in a 0 or 1 state. Because of its capacity to deal with several states simultaneously, quantum computers have the potential to be far more powerful than conventional computers (Shyam &Khang et al., 2023).
Various approaches, such as Ion Trap and Quantum Dot techniques, have been used to show the notion of quantum computing. When you read the word “quantum,” you may envision an atom or molecule. Quantum computers operate on the same principles. Processing takes place at the bit level in a conventional computer. Quantum computers are governed by a certain behavior known as quantum physics. There are several ways in quantum physics for characterizing the interaction between different atoms. These atoms are referred to as “qubits” in the context of Quantum Computers. (Chatterjee & Chakraborty, 2020). Quantum computing evolved from the notion of quantum mechanics, which was created by physicists such as Max Planck and Albert Einstein in the early twentieth century.
In 1982, scientist Richard Feynman advocated employing quantum mechanics for computation, claiming that quantum computers might handle issues that were difficult or impossible for conventional computers. Peter Shor, a mathematician, created a quantum method in 1994 that could effectively factor enormous numbers, which has important implications for encryption. This finding heightened interest in quantum computing. Experimental demonstrations of fundamental quantum computing processes were obtained in the late 1990s and early 2000s utilizing diverse approaches such as ion traps and quantum dots. These investigations lay the groundwork for the practical application of quantum computers. Since then, advances in qubit technology, error correction methods, and quantum algorithms have fueled continued research and development in quantum computing (Hidary & Hidary, 2020).
Quantum computing offers the ability to tackle complicated problems that traditional computers are incapable of solving, notably in the realm of quantum mechanics. Quantum computers may do certain computations significantly quicker than traditional computers, which has important ramifications for a variety of sectors like cryptography, optimization, and drug development. It can improve machine learning algorithms by more effectively processing and analyzing massive volumes of data, resulting in greater pattern detection and optimization. These are capable of simulating quantum systems, allowing scientists to examine and comprehend events at the atomic and molecular levels, which has applications in materials science, chemistry, and physics.
By tackling complicated optimization problems more effectively, quantum computing can help to breakthroughs in disciplines such as weather forecasting, financial modeling, and logistics optimization (Gill et al., 2022). The quantum computing field is quickly developing and offers several opportunities in virtually every subject, but the most essential aspect is its capacity to aid farmers in enhancing agricultural efficiency and productivity (Khang & Agriculture et al., 2023). A quantum computing platform might also aid in the development of chemicals used in the production of energy-efficient fertilizers. These considerations have contributed to the increasing use of quantum computing technology in agriculture (Khang & Santosh et al., 2023).