We have ubiquitous presence of computers today, ranging from simple controllers in modern appliances to smartphones in our pockets that provide a wide range of everyday services, to powerful supercomputers and large data centers that carry out the most computationally intensive tasks. These computational machines have a few things in common: for example, the information they handle is stored in bits (0 or 1), and the procedure for processing the information is specified by a program. A great deal is known about the limits of what such computational machines can and cannot do efficiently. There are many important computational problems that are believed to be very difficult to solve using even the most powerful computers, where the resource requirement-whether it is the size of the machine or the time it takes to finish the task-increases exponentially as a function of the problem size.
We have ubiquitous presence of computers today, ranging from simple controllers in modern appliances to smartphones in our pockets that provide a wide range of everyday services, to powerful supercomputers and large data centers that carry out the most computationally intensive tasks. These computational machines have a few things in common: for example, the information they handle is stored in bits (0 or 1), and the procedure for processing the information is specified by a program. A great deal is known about the limits of what such computational machines can and cannot do efficiently. There are many important computational problems that are believed to be very difficult to solve using even the most powerful computers, where the resource requirement-whether it is the size of the machine or the time it takes to finish the task-increases exponentially as a function of the problem size.
Computation is a process of performing a large number of simple operations. The main building blocks of classical computers are bits. Each of them can take either the value 0 or 1. A series of gates can change these values by acting on them selectively. This process is structured to fulfil a purpose. For example, if we are interested in adding two numbers, the numbers are encoded into bits and gates are performed such that the final state of the bits reveals the desired answer: a number which is the sum of the initial ones. While it is possible to build algorithms that can perform almost any computation, it is also desirable to find the answer within a reasonable length of time or with a reasonable amount of resources. There is an ever-growing demand in computational power for both scientific and commercial purposes. Indeed, it is surprisingly easy to find an application that can jam even the fastest computer. This fuels a vast effort in the research of information science to increase the speed and processing power of computers. Modern computational models are based on the universal Turing machine (Turing, 1937). This is a theoretical information processing model that employs the elementary gate processes described above. It can efficiently simulate any other device capable of performing an algorithmic process. Since the introduction of the Turing machine, physics has influenced computation in many ways.
Modern computing has achieved extraordinary scale and speed. Electronic computers emerged commercially in the mid-20th century, and classical computing has since scaled through architectural advances and parallelism. However, many scientifically important problems remain computationally expensive because the underlying state spaces grow exponentially. Quantum computing addresses this challenge by representing and transforming information using quantum-mechanical state vectors and unitary operations. These conventional systems are referred to as classical computers, capable of handling everyday tasks with moderate computational power and speed. Classical computers represent information using bits and process it using deterministic logic operations. While modern classical systems use extensive parallelism (multi-core CPUs, GPUs, and clusters), many important problems still scale poorly because the resources required grow rapidly with input size. Even with significant parallelism, complex problems that involve large combinatorial structures or high-dimensional state spaces often require substantial computational time and resources, motivating the need for more powerful computational paradigms capable of handling such workloads.
The quantum world is fascinating. It presents a description of nature that defies our most rooted concepts about what reality is. For example, quantum objects possess \lq\lq spooky\rq\rq\ properties that allow them to be in multiple places at the same time, to move in different directions simultaneously, or to exist and not to exist. In other words, to live several parallel stories. Making use of these parallel stories to compute is the realm of quantum computation, a world in which information is processed using the laws of quantum physics. Nowadays, quantum computers are an object of an extensive experimental and theoretical research, since it is known that, on paper, these can solve many of the current computational challenges. These are the problems that even the most sophisticated supercomputers of today, or tomorrow, will not be able to solve.
A cluster of interconnected devices able to communicate with each other is denoted as the Internet of Things (IoT). These devices can communicate with each other and exchange data over the Internet without human intervention. A range of daily usable devices, such as wearable devices, smartphones, and home appliances, are programmed in the IoT with sensors and industrial machines in various industries. The proliferation of the IoT is to collect and exchange data for task automation, improving efficiency, and providing new services. Quantum computing incorporates quantum-mechanical phenomena to perform operations on data, allowing for much faster computation than classical computers in certain tasks. This chapter will summarize a concrete idea in the field of artificial intelligence (AI); quantum computing has the qualities of significantly accelerating AI algorithms, particularly in areas such as machine learning, optimization, and simulation. With the integration of the latest techniques, quantum AI will address and solve the complex problems in many applications, such as healthcare, security, and industrial IoT.
This is article examines the issue of new conceptual architectures of ultrafast hybrid computing devices based on digital technologies, where the size of an elementary computing device approaches the size of a molecule or even an atom. At this level, the laws of classical physics stop working and quantum laws begin to operate, which for many important dynamic problems have yet to be described theoretically. For such practically significant examples of the use of quantum computers as the training of language models, as well as the analysis of the effectiveness of model training on specified chips and with specified train characteristics (model depth, dataset size, etc.), a new concept for the use of hybrid computing platforms in data centers has emerged. Hybrid computing platforms for data centers represent the integration of various computing resources, including on-premises servers, cloud solutions and virtualized environments. These platforms allow organizations to optimize their computing power, providing flexibility, scalability and costeffectiveness. With the increase in data volumes and the need for cloud services, there is an increase in the creation of data centers. The edge-computing extension, which supports real-time processing at data collection points, matches the potential of quantum computing for revolutionary tasks such as optimization and decision-making. As peripheral systems evolve, the integration of algorithms with quantum technologies can expand their capabilities. This raises pitfalls in evaluating processor performance in hybrid computers, which can vary depending on many factors, including the processor architecture, the types of cores used (for example, high-performance and energy-efficient), as well as the nature of the tasks performed. In this study, a mathematical model of the presented systems is proposed as a combination of combining various concepts that will help to understand and evaluate the performance of hybrid computing, allowing you to optimize the use of resources and improve overall efficiency. Today, there is a growing dependence on hybrid models - mixing local, cloud and peripheral systems, which further emphasizes the need for an infrastructure that accommodates both classical and quantum resources. Quantum computing is increasingly seen as an important part of these hybrid architectures, preparing organizations to meet the new challenges of the advanced computing landscape.