The article is devoted to the issues of quantum computing and algorithms aimed at implementing quantum machine learning both on separate quantum processors and in hybrid circuits using TPU and CPU.
Currently, pattern recognition is an extremely important activity in several fields.Approaches using artificial intelligence are one of the means of detecting these patterns and, therefore, optimizations in these methods are essential for such recognition to be carried out efficiently, both in processing time and in the hit rate.The field of machine learning, one of the paradigms of artificial intelligence, dates back to the beginnings of modern computing and in recent decades has been used intensively in pattern recognition.Despite the technical challenges related to the hardware, quantum computing has also been studied for a better performance of multiple computational tasks that are often unfeasible to be performed by classical algorithms.In this context, one of the fields that gain focus is the use of quantum circuits in machine learning algorithms.Thus, this work aims to compare the performance and complexity of classical algorithms, used in machine learning, to their quantum and hybrid versions, in order to identify the most efficient implementations.Another aspect to highlight is the feasibility of applying such models at certain scales, taking into account the limitations of current hardware.This will be done through a review of current academic production, followed by the development, implementation, testing, and analysis of the performance and complexity of both approaches.It is expected with this experiment that it will be possible to verify the effective possibility of using machine learning algorithms in their hybrid versions, either in current cases or in the near future.
Here we discuss advances in the field of quantum machine learning. The following document offers a hybrid discussion; both reviewing the field as it is currently, and suggesting directions for further research. We include both algorithms and experimental implementations in the discussion. The field's outlook is generally positive, showing significant promise. However, we believe there are appreciable hurdles to overcome before one can claim that it is a primary application of quantum computation.
This article examines the concept of synergy between quantum machine learning and classical optimization methods in the context of routing problems. The need to integrate quantum variational algorithms with classical relaxation, heuristics, and decomposition methods to ensure computational efficiency and stability is substantiated. Particular attention is paid to the role of the classical component in generating quality estimates, parameter management, and implementing post-processing of quantum samples. Prospects for the development of hybrid algorithms in the areas of scalability, reducing noise sensitivity, and improving the accuracy of solutions in complex combinatorial settings are discussed.
Quantum Computing (QC) has arisen as a possible solution to the computational problems arising in the background of modern Electrical Engineering (EE) applications when processing high-dimensional data generated by smart grids, ultra-high-speed communications, and other dense sensor networks. This review assesses specifically the intersection of Quantum Signal Processing (QSP) and Quantum Machine Learning (QML) in the EE context. Here, 30 research articles published between 2017 and 2025 are analyzed. Research articles are selected via structured database searches based on relevance, depth of technical detail, and connection to EE domains. Via quantum models (e.g., gate-based, annealing), base types of signal processing tasks (e.g., filtering and Fourier transforms), and ML use cases (e.g., classification, regression, and clustering), the review classifies the methods. Moreover, the review highlights the use of quantum-enhanced algorithms, which exhibit theoretical benefits to reduce complexity for large-scale signal processing and learning problems, but have limited use cases in practice. Findings from Quantum-enhanced algorithms indicate theoretical potential for reducing complexity in large-scale signal processing and Machine Learning (ML), though real-world use is limited by hardware, noise, and scalability issues. Key gaps remain in benchmarking against classical methods and developing hybrid models. While still emerging, QSP and QML offer promising solutions for future EE challenges, highlighting the need for continued research and collaboration.