Machine learning is frequently listed among the most promising applications for quantum computing. This is in fact a curious choice: Today's machine learning algorithms are notoriously powerful in practice, but remain theoretically difficult to study. Quantum computing, in contrast, does not offer practical benchmarks on realistic scales, and theory is the main tool we have to judge whether it could become relevant for a problem. In this perspective we explain why it is so difficult to say something about the practical power of quantum computers for machine learning with the tools we are currently using. We argue that these challenges call for a critical debate on whether quantum advantage and the narrative of 'beating' classical machine learning should continue to dominate the literature the way it does, and highlight examples for how other perspectives in existing research provide an important alternative to the focus on advantage.
Machine learning is frequently listed among the most promising applications for quantum computing. This is in fact a curious choice: Today's machine learning algorithms are notoriously powerful in practice, but remain theoretically difficult to study. Quantum computing, in contrast, does not offer practical benchmarks on realistic scales, and theory is the main tool we have to judge whether it could become relevant for a problem. In this perspective we explain why it is so difficult to say something about the practical power of quantum computers for machine learning with the tools we are currently using. We argue that these challenges call for a critical debate on whether quantum advantage and the narrative of 'beating' classical machine learning should continue to dominate the literature the way it does, and highlight examples for how other perspectives in existing research provide an important alternative to the focus on advantage.
Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed.
Quantum machine learning, a cutting-edge topic of study, brings together the frontiers of quantum computing with classical machine learning. Quantum computing and machine learning may be able to help solve problems in each other's fields of expertise. Due to the surge of new data, there is growing fear that current machine learning algorithms may be exceeding their computational constraints. Thus, in certain machine learning tasks, quantum computing can be an advantage. Quantum machine learning research focuses on the creation and deployment of quantum software that could speed up machine learning in comparison to standard computing systems. Because of improvements in computing power and algorithmic innovation, machine learning approaches have emerged as powerful tools for detecting patterns in data. Because quantum systems exhibit unusual patterns that classical systems are unable to produce efficiently, it is plausible to assume that quantum computers will outperform classical computers on machine learning tasks. The following is a summary of the current state of quantum machine learning research.
Quantum computing has been a source of hype in the past few years—especially in its application to machine learning (ML) use-cases. This paper compares QML algorithms to traditional ML for a variety of classification problems yielding an overview on potential applications that quantum technologies can either complement or provide superior performance. The paper begins by outlining the fundamental principles of quantum computing required for its application in machine learning. This article will highlight key ideas such as quantum parallelism and the use of quantum interference in n-computing efficiency. The conversation turns to Quantum machine learning Algorithms, in comparison with Classical algorithms without any further ado and specific quantum algorithmic extensions which are available for the problem of machines. These quantum algorithms are analyzed in terms of computational speed and accuracy, leveraging qubits to potentially accelerate learning processes and achieve improved results. Both the prospects and challenges of quantum algorithms for practical ML applications, by combining empirical data with insights from theory. The study also investigates how these limitations of quantum computing today such as qubit coherence, error rates and scalability are likely to impact the practical deployment of QML. In the final sections, the paper discusses next steps and future directions for quantum machine learning with a focus on potential milestones that could lift current obstacles to merge more deeply classical and quantum tools of data analysis. The article provides an in-depth data-driven perspective on the emerging intersection of these two fields with a view towards their potential future impact within quantum machine learning.
Quantum computing has generated significant expectations for its potential to enhance machine learning (ML) tasks, with theoretical research proposing computational advantages ranging from polynomial to exponential speedups for specific subroutines.However, a critical disconnect remains: theoretical or asymptotic speedup claims do not automatically imply a practical, end-to-end advantage.Current evidence does not clearly establish whether these claimed advantages translate into measurable superiority over optimised classical approaches.This review critically assesses the relationship between theoretical claims of quantum advantage and the currently available practical and experimental evidence.We critically examine the evidence and assumptions underlying claimed speedups, including the overheads associated with data encoding and access, as well as the use of appropriate and competitive classical baselines for fair comparison.The scope of this assessment covers key limiting factors, such as scalability, trainability, hardware noise in the noisy intermediate-scale quantum (NISQ) era, and overall resource requirements.By distinguishing between theoretical potential and demonstrated practical performance, this work identifies where current evidence is convincing and where it remains insufficient.The review identifies the specific conditions required for the credible validation of quantum advantage and argues that future progress must be positioned around rigorous, realistic, and end-to-end evaluation rather than theoretical complexity claims alone.
As the limits of classical machine learning (ML) become increasingly evident—especially in terms of computational scalability and data processing—quantum computing emerges as a promising frontier capable of addressing these challenges. Quantum Machine Learning (QML) represents the intersection of quantum computing and traditional ML, aiming to leverage quantum phenomena such as superposition, entanglement, and quantum parallelism to enhance learning capabilities, accelerate training, and solve complex problems that are intractable for classical algorithms. This paper provides a comprehensive overview of the evolving field of QML, beginning with the theoretical foundations of quantum computing and its synergy with classical machine learning principles. We explore key QML algorithms, including quantum support vector machines (QSVM), variational quantum classifiers (VQC), quantum neural networks (QNN), and hybrid quantum-classical approaches that exemplify the practical integration of both paradigms. We further examine real-world applications of QML across domains such as drug discovery, finance, climate modeling, and optimization, where quantum enhancements have begun to show early but promising results. Despite its theoretical potential, QML faces substantial limitations, including quantum hardware instability, limited qubit scalability, noise sensitivity, and unresolved challenges in quantum data encoding and interpretability. In synthesizing recent advancements and persistent obstacles, this paper highlights the importance of continued interdisciplinary research, algorithmic refinement, and hardware evolution. We also outline future directions that could lead QML from experimental promise to practical utility, including developments in quantum error correction, benchmarking standards, and application-specific quantum architectures
Encouraged by growing computing power and algorithmic development, machine learning technologies have become powerful tools for a wide variety of application areas, spanning from agriculture to chemistry and natural language processing. The use of quantum systems to process classical data using machine learning algorithms has given rise to an emerging research area, i.e. quantum machine learning. Despite its origins in the processing of classical data, quantum machine learning also explores the use of quantum phenomena for learning systems, the use of quantum computers for learning on quantum data and how machine learning algorithms and software can be formulated and implemented on quantum computers. Quantum machine learning can have a transformational effect on computer science. It may speed up the processing of information well beyond the existing classical speeds. Recent work has seen the development of quantum algorithms that could serve as foundations for machine learning applications. Despite its great promise, there are still significant hardware and software challenges that need to be resolved before quantum machine learning becomes practical. In this paper, we present an overview of quantum machine learning in the light of classical approaches. Departing from foundational concepts of machine learning and quantum computing, we discuss various technical contributions, strengths and similarities of the research work in this domain. We also elaborate upon the recent progress of different quantum machine learning approaches, their complexity, and applications in various fields such as physics, chemistry and natural language processing.