Artificial intelligence and quantum computing have both seen tremendous progress in recent years, opening up new avenues for accelerating scientific discovery. Classical machine learning has already proven to be useful for addressing challenges in quantum computation; and hardware progress is well underway towards the early fault-tolerant regime. On the other hand, the emerging field of quantum machine learning is aimed at utilizing the strengths of both computational paradigms, via the development of learning algorithms or models that benefit from running on quantum devices or from training on quantum data. We therefore argue that both of these paradigms stand to benefit substantially from each other. Here we review the most salient opportunities for machine learning in quantum computing, hoping to inspire a virtuous cycle through which both fields can mutually benefit.
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This review examines how classical machine learning can accelerate quantum computing and how quantum resources might improve learning, asking where these directions could form a mutually reinforcing development cycle. It surveys existing literature and proposes research priorities rather than presenting new algorithms, proofs, experiments, or numerical benchmarks.
The classical-ML-for-quantum-computing survey covers error-correction decoding, code discovery, application discovery, quantum control, hardware–software co-design, circuit-architecture search, and neural quantum states. The quantum-learning survey covers direct learning from quantum data, quantum-generated training data for classical models, communication and memory advantages, variational models, reinforcement learning, kernels, linear algebra, and optimization. It distinguishes computational speedups from unconditional sample, memory, or communication separations. Among the reviewed results are exponential sample-complexity advantages from processing quantum states coherently and trainable generative models whose distributions are classically hard to sample. These are results from cited work, not established here.
The central proposed pathway is to use quantum simulation to generate training data for classical chemistry and many-body models, with improved classical models subsequently supporting quantum-device development. Chemistry, many-body physics, and quantum games serve as illustrative case studies.
Stated limitations include data-loading and readout costs, measurement overhead, barren plateaus, classical simulability and dequantization, hardware latency, and quantum-transduction requirements. Practical applications of several unconditional separations remain unclear; many proposed feedback mechanisms are explicitly speculative. The review calls for standardized quantum-data benchmarks and empirical testing as hardware improves.
Some signs of AI-generated textOverclaimsPossible error
Error found: The synthesis claims provably classically inaccessible improvements from quantum-generated data in every case study, although the chemistry and game-play discussions do not establish such application-specific separations.
The QNN discussion uses reversible simulation and inclusion of a classical submodel to motivate preserved trainability, but those arguments do not establish trainability when the added quantum parameters are optimized.
The unconditional-advantages discussion incorrectly states that an n-qubit state can be teleported using n classical bits; standard arbitrary-state teleportation requires 2n classical bits and n shared Bell pairs.
Written by gpt-6.1-sol from the paper's full text. Automated: it can be wrong.
Artificial intelligence and quantum computing have both seen tremendous progress in recent years, opening up new avenues for accelerating scientific discovery. Classical machine learning has already proven to be useful for addressing challenges in quantum computation; and hardware progress is well underway towards the early fault-tolerant regime. On the other hand, the emerging field of quantum machine learning is aimed at utilizing the strengths of both computational paradigms, via the development of learning algorithms or models that benefit from running on quantum devices or from training on quantum data. We therefore argue that both of these paradigms stand to benefit substantially from each other. Here we review the most salient opportunities for machine learning in quantum computing, hoping to inspire a virtuous cycle through which both fields can mutually benefit.
This review examines how classical machine learning can accelerate quantum computing and how quantum resources might improve learning, asking where these directions could form a mutually reinforcing development cycle. It surveys existing literature and proposes research priorities rather than presenting new algorithms, proofs, experiments, or numerical benchmarks.
The classical-ML-for-quantum-computing survey covers error-correction decoding, code discovery, application discovery, quantum control, hardware–software co-design, circuit-architecture search, and neural quantum states. The quantum-learning survey covers direct learning from quantum data, quantum-generated training data for classical models, communication and memory advantages, variational models, reinforcement learning, kernels, linear algebra, and optimization. It distinguishes computational speedups from unconditional sample, memory, or communication separations. Among the reviewed results are exponential sample-complexity advantages from processing quantum states coherently and trainable generative models whose distributions are classically hard to sample. These are results from cited work, not established here.
The central proposed pathway is to use quantum simulation to generate training data for classical chemistry and many-body models, with improved classical models subsequently supporting quantum-device development. Chemistry, many-body physics, and quantum games serve as illustrative case studies.
Stated limitations include data-loading and readout costs, measurement overhead, barren plateaus, classical simulability and dequantization, hardware latency, and quantum-transduction requirements. Practical applications of several unconditional separations remain unclear; many proposed feedback mechanisms are explicitly speculative. The review calls for standardized quantum-data benchmarks and empirical testing as hardware improves.
Quantum computing and machine learning (ML) are converging to form a new paradigm in computational science, offering transformative capabilities in data processing, optimization, and pattern recognition. Quantum systems leveraging superposition, entanglement, and parallelism enable theoretical speedups for complex ML tasks that challenge classical architectures. Emerging quantum algorithms, such as quantum support vector machines (QSVMs), quantum principal component analysis (QPCA), and variational quantum circuits (VQCs), show promise for addressing high-dimensional, computationally intensive problems. Despite current limitations posed by noisy qubits, limited coherence times, and data encoding constraints, progress is accelerating. Hybrid quantum–classical models, noise-resilient techniques, and expanding quantum cloud infrastructures are enhancing the practical reach of quantum machine learning (QML). Applications in molecular modeling, optimization, and cybersecurity are among the early areas showing tangible potential. With the eventual advent of fault–tolerant quantum processors, QML could enable breakthroughs across disciplines, from drug discovery and materials science to next-generation artificial intelligence (AI). As the volume and complexity of data continue to grow, QML is positioned to redefine the future of intelligent computation.
Over the past decade, the field of Classical Machine Learning has experienced rapid growth, primarily driven by the widespread adoption of the internet and the increasing interconnectivity of digital devices.This expansion has led to the generation of vast volumes of data requiring effective analysis and processing.Machine Learning, as a subfield of Artificial Intelligence, leverages tools mainly from Statistics to identify patterns and extract meaningful insights from data without the need for explicit programming.At the same time, Quantum Computing introduces a fundamentally different paradigm for information processing, utilizing principles of Quantum Mechanics, such as superposition and entanglement.The convergence of these two scientific domains has given rise to the emerging field of Quantum Machine Learning.The present thesis aims to investigate whether the integration of Machine Learning and Quantum Computing can offer tangible advancements in the efficiency and performance of Artificial Intelligence systems.Based on current research findings, this study explores the potential, advantages, and limitations of this interdisciplinary approach, as well as its prospects for development in the coming years.
There has been remarkable development in the data-driven decision-making domain of Artificial Intelligence (AI). However, the complexity of the AI models and the amount of data have shown some drawbacks with conventional computing models. One of the interdisciplinary branches of quantum computing and machine learning is Quantum Machine Learning (QML), which is a combination of quantum computing and machine learning and relies on the unique characteristics of quantum computing, such as superposition, entanglement, and quantum parallelism, to enhance the performance of the machines. This review provides a comprehensive description of theory, architectures, algorithms, implementational approaches, applications, problems, and future directions of QML. The core concepts of quantum computing and machine learning are introduced. Different representations of quantum states, quantum data encoding, variational quantum circuits, quantum kernel methods, and hybrid quantum or classical optimization are discussed. Significant QML algorithms like Quantum Support Vector Machines, Quantum Neural Networks, Variational Quantum Eigen solvers, Quantum Approximate Optimization Algorithms, and Quantum reinforcement learning are explored and evaluated for computational power and applications. The scaling up of quantum computer sizes, data encoding overhead, complexity of optimizations, benchmarking, and decoherence are discussed in detail, as well as recent developments in fault-tolerant quantum computing and hybrid learning approaches. Finally, a conceptual framework is given to demonstrate the unification of classical computation and quantum computation to intelligently process data. This review demonstrates how QML could be the next-generation computational method to accelerate scientific advancements and enable new advanced AI technologies.
Quantum information technologies, on the one hand, and intelligent learning systems, on the other, are both emergent technologies that are likely to have a transformative impact on our society in the future. The respective underlying fields of basic research-quantum information versus machine learning (ML) and artificial intelligence (AI)-have their own specific questions and challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question of the extent to which these fields can indeed learn and benefit from each other. Quantum ML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently we have witnessed significant breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups for ML problems, critical in our 'big data' world. Conversely, ML already permeates many cutting-edge technologies and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been (theoretically) demonstrated for interactive learning tasks, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement-exploring what ML/AI can do for quantum physics and vice versa-researchers have also broached the fundamental issue of quantum generalizations of learning and AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is fully described by quantum mechanics. In this review, we describe the main ideas, recent developments and progress in a broad spectrum of research investigating ML and AI in the quantum domain.
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
There is a major promise of extensive technical disturbance made by increasing convergence of two rapid development areas: Quantum Computing & Artificial Intelligence (AI). Unlike traditional computers, quantum computer use quantum -mechanical concepts as complication & superposition towards process data in a new way. Because of this, quantum computer machines can improve traditional systems towards solve certain types of problems. Machine learning (ML) algorithm is a sub -region of artificial intelligence aimed at automatically analyzing, interpreting & predicting procedures on a dataset. It is expected that the merger of AI & quantum calculation can quickly increase the processor power available for machine learning, which will accelerate processes such as data analysis, mimic quantum systems & handle complex customization problems. Medication development, material research & encryption abide some of the areas that benefit from the correction of quantum calculation of AI. among Quantum-Enhanced AI, the training machine learning models can endure very quick & more effective, so we can take problems that were impossible in the past. Pattern recognition, classification & decision -making artificial intelligence abide corners of applications; Quanta algorithms can also provide better ways for these features. Nevertheless, there abide still obstacles towards removing, such as creating special quantum algorithms for AI functions, ensuring that quantum systems can fix incorrectly, & make quantum machine scalable. Despite these challenges, the combination of AI & quantum calculation has the opportunity towards enter a new era of technical progress, which has not been able towards solve traditional data processing by answering major, complex problems so far.