The quantum core method is one of the most important methods in quantum machine learning.However, the number of features used for quantum nuclei is limited to a few dozen features.The block product state structure is used as a quantum feature map and the implementation ofprogrammable gate matrices is demonstrated. The relevance of these studies lies in the mathematicaland software modeling and implementation of a quantum computing system as part of thedevelopment of the implementation of a quantum core on FPGA for solving classes of problems ofa classical nature. The scientific novelty of this research area is the development of a hybrid simulatorof the quantum cores of a central processing unit (CPU) and a programmable logic integratedcircuit (FPGA) several orders of magnitude faster than a conventional quantum computingsimulator. This joint development of the implemented quantum core and its efficient FPGA implementation allowed numerical simulation of the quantum core based on gates in terms of inputfeatures, up to 780-dimensional features using 4000 samples. We applied the quantum kernel toimage classification problems using the Fashion-MNIST dataset and showed that the quantumkernel is comparable to Gaussian kernels with optimized throughput. The analysis of the work inthis field has shown that a new qualitative level has now been reached, opening up promising opportunitiesfor the implementation of multi-qubit quantum computing. The prospects for implementationand development are connected not only with technological capabilities, but also with solvingthe issues of building effective quantum systems for solving actual mathematical problems,cryptography problems and control (optimization) problems.
The quantum core method is one of the most important methods in quantum machine learning.However, the number of features used for quantum nuclei is limited to a few dozen features.The block product state structure is used as a quantum feature map and the implementation ofprogrammable gate matrices is demonstrated. The relevance of these studies lies in the mathematicaland software modeling and implementation of a quantum computing system as part of thedevelopment of the implementation of a quantum core on FPGA for solving classes of problems ofa classical nature. The scientific novelty of this research area is the development of a hybrid simulatorof the quantum cores of a central processing unit (CPU) and a programmable logic integratedcircuit (FPGA) several orders of magnitude faster than a conventional quantum computingsimulator. This joint development of the implemented quantum core and its efficient FPGA implementation allowed numerical simulation of the quantum core based on gates in terms of inputfeatures, up to 780-dimensional features using 4000 samples. We applied the quantum kernel toimage classification problems using the Fashion-MNIST dataset and showed that the quantumkernel is comparable to Gaussian kernels with optimized throughput. The analysis of the work inthis field has shown that a new qualitative level has now been reached, opening up promising opportunitiesfor the implementation of multi-qubit quantum computing. The prospects for implementationand development are connected not only with technological capabilities, but also with solvingthe issues of building effective quantum systems for solving actual mathematical problems,cryptography problems and control (optimization) problems.
Currently, the theory of quantum computing is actively developing. Despite the fact that a quantum computer has some features, any algorithm designed for a classical computer can be implemented for a quantum one. The relevance of the topic of quantum computers is confirmed by the fact that even now banking systems, cryptography and encryption of information require enormous resources of computing systems, these are just some examples, but the most important and vivid ones that relate to the everyday life of a huge part of the world's population. A hardware computing core has been developed and simulated, which is the main part of an accelerator based on field-programmable gate array with computer aided design Altera Quartus. Considering the obtained time dependence of the number of cycles required for the impact of a single-qubit quantum gate on a quantum register on the number of qubits and parallel arithmetic logic units in the hardware computing core, when simulating quantum computing, a method was developed to determine the increase in hardware performance and ways to improve the performance of an accelerator based on a hardware computing core were developed. based on field-programmable gate array.
Recently, there has been a rapid increase in interest in quantum computers. Their work isbased on the use of quantum-mechanical phenomena such as superposition and entanglement forcomputing to transform input data into outputs that can actually provide effective performance3–4 orders of magnitude higher than any modern computing devices, which will allow solving theabove and others. tasks in real- and accelerated-time scale. This article is devoted to solving theproblem of research and development of methods for optimizing quantum computing within theframework of the application of quantum accelerators. A block diagram of a hardware acceleratoris proposed to increase the performance of simulated quantum computing. The development of thestructural diagram of the communication module of the hardware accelerator and the softwaremodel was carried out. The relevance of these studies lies in mathematical and software modelingand implementation of correction codes for correcting several types of quantum errors in the developmentand implementation of quantum algorithms for solving classes of problems of a classicalnature. The scientific novelty of this direction is expressed in the elimination of one of the disadvantagesof the quantum computational process. The scientific novelty of this area is primarilyexpressed in the constant updating and supplementation of the field of quantum research in anumber of areas, and the computer simulation of quantum physical phenomena and features ispoorly covered in the world.
Quantum computers acting as ML accelerators have attracted considerable interest as a promising avenue for addressing the limitations of classical computing confronted with solving complex, high-dimensional problems and handling large data sets. The most exciting aspect of quantum computing lies in its quantum advantage via techniques such as quantum parallelism, superposition, and entanglement which allows not only for rapid data processing, but also a means to resolve complex optimization problems beyond the capacity of classical algorithms. Nevertheless, the usage of quantum computing in machine learning is still a fledgling domain, with challenges concerning quantum noise, hardware restrictions, and algorithmic development. In particular, this research is concerned with the development of and study on hybrid quantum-classical algorithms that target to improve ML models in terms of computational capabilities by making use of the best of both worlds. To address this, researchers are developing hybrid algorithms that leverage the strengths of quantum computing for specific tasks, like optimization and pattern recognition, while still relying on classical computing for more flexible and larger-scale processing, ultimately achieving more efficient machine learning systems that can handle large datasets and complex problem-solving more effectively. It includes potential hybrid approaches like quantum-enhanced optimization algorithms, quantum neural networks, and quantum support vector machines, and examines how such algorithms can be used for practical ML problems including data classification, regression and clustering. Furthermore, the paper proposes that hybrid quantum algorithms offer several advantages over their classical counterparts, such as faster convergence rates, higher accuracy in high-dimensional spaces, and improved handling of non-linear and complex problem landscapes. Key advancements in the area of data processing speed and efficiency in problem-solving with and without quantum in the machine learning workflow are also highlighted in the research through comparative analysis and simulation studies. Lastly, the paper concludes by outlining the obstacles yet to be surmounted and the opportunities that lie ahead for hybrid quantum-classical algorithms, such as further improvement of quantum hardware and error mitigation methods, as well as the design of new algorithmic paradigms that seamlessly integrates the classical and quantum computing spaces.
The quantum kernel method has attracted considerable attention in the field of quantum machine learning. However, exploring the applicability of quantum kernels in more realistic settings has been hindered by the number of physical qubits current noisy quantum computers have, thereby limiting the number of features encoded for quantum kernels. Hence, there is a need for an efficient, application-specific simulator for quantum computing by using classical technology. Here we focus on quantum kernels empirically designed for image classification and demonstrate a field programmable gate arrays (FPGA) implementation. We show that the quantum kernel estimation by our heterogeneous CPU-FPGA computing is 470 times faster than that by a conventional CPU implementation. The co-design of our application-specific quantum kernel and its efficient FPGA implementation enabled us to perform one of the largest numerical simulations of a gate-based quantum kernel in terms of features, up to 780-dimensional features. We apply our quantum kernel to classification tasks using the Fashion-MNIST dataset and show that our quantum kernel is comparable to Gaussian kernels with the optimized hyperparameter.
The quantum computing represents a new field, which is still being researched, that may have numerous applications in the computer arithmetic, in the encryption - decryption systems, rapid search algorithms and physical systems' emulations. The hardware device that can make this type of calculations is still expensive nowadays and the number of the manufacturers is reduced. On the other hand, the simulation of a quantum computer using the devices currently available on the market - like the FPGA digital circuits - helps in getting an overview of the possibilities that the quantum calculations can provide and it can also be used when simulating physical processes in general. The aim of this article is to present our own solution of an emulator for the quantum calculations using the digital modules implemented in FPGA.
Quantum error correction (QEC) is required in quantum computers to mitigate the impact of errorson physical qubits. The goal is to optimize the neural network for high decoding performance whilemaintaining a minimalistic hardware implementation. The errors associated with decoherence can bereduced by adopting QEC schemes that encode multiple imperfect physical qubits into a logical quantumstate, similar to classical error correction. The relevance of these studies lies in the mathematicaland software modeling and implementation of corrective codes to correct several types of quantumerrors in the development and implementation of quantum algorithms for solving classes of problems ofa classical nature. The scientific novelty of this direction is expressed in the elimination of one of theshortcomings of the quantum computing process. The development of the theory and principles for constructingmodeling systems that are resistant to external interference (dependence of data distortion onnoise, dependence of the error of a quantum computing process on the measure and purity of entanglement)for modeling quantum computing is a dynamic area, as evidenced by a large number of existingmodels reflecting certain quantum computational processes and phenomena (quantum teleportation,parallelism, entanglement of quantum states) and scientific papers. Although quantum computing is notyet ready to move from theory to practice, it is nevertheless possible to reasonably guess what form aquantum computer might take, or, more importantly for programming language design, what interface itwould be possible to interact with such a quantum computer. It is natural to apply the lessons learnedfrom the programming of classical computing to quantum computing. The analysis of works in this areashowed that a new qualitative level has now been reached, which opens up promising opportunities forthe implementation of multi-qubit quantum computing. Prospects for implementation and developmentare associated not only with technological capabilities, but also with the solution of issues of buildingeffective quantum systems for solving actual mathematical problems, cryptography problems and control(optimization) problems.