Quantum computing technology is advancing rapidly. Yet, even accounting for these trends, a quantum leap would be needed for quantum computers to meaningfully impact deep learning over the coming decade or two. We arrive at this conclusion based on a first-of-its-kind survey of quantum algorithms and how they match potential deep learning applications. This survey reveals three important areas where quantum computing could potentially accelerate deep learning, each of which faces a challenging roadblock to realizing its potential. First, quantum algorithms for matrix multiplication and other algorithms central to deep learning offer small theoretical improvements in the number of operations needed, but this advantage is overwhelmed on practical problem sizes by how slowly quantum computers do each operation. Second, some promising quantum algorithms depend on practical Quantum Random Access Memory (QRAM), which is underdeveloped. Finally, there are quantum algorithms that offer large theoretical advantages, but which are only applicable to special cases, limiting their practical benefits. In each of these areas, we support our arguments using quantitative forecasts of quantum advantage that build on the work by Choi et al. [2023] as well as new research on limitations and quantum hardware trends. Our analysis outlines the current scope of quantum deep learning and points to research directions that could lead to greater practical advances in the field.
Quantum computing technology is advancing rapidly. Yet, even accounting for these trends, a quantum leap would be needed for quantum computers to meaningfully impact deep learning over the coming decade or two. We arrive at this conclusion based on a first-of-its-kind survey of quantum algorithms and how they match potential deep learning applications. This survey reveals three important areas where quantum computing could potentially accelerate deep learning, each of which faces a challenging roadblock to realizing its potential. First, quantum algorithms for matrix multiplication and other algorithms central to deep learning offer small theoretical improvements in the number of operations needed, but this advantage is overwhelmed on practical problem sizes by how slowly quantum computers do each operation. Second, some promising quantum algorithms depend on practical Quantum Random Access Memory (QRAM), which is underdeveloped. Finally, there are quantum algorithms that offer large theoretical advantages, but which are only applicable to special cases, limiting their practical benefits. In each of these areas, we support our arguments using quantitative forecasts of quantum advantage that build on the work by Choi et al. [2023] as well as new research on limitations and quantum hardware trends. Our analysis outlines the current scope of quantum deep learning and points to research directions that could lead to greater practical advances in the field.
At the nexus of quantum computing and machine learning, quantum machine learning (QML) and quantum deep learning (QDL) have emerged as promising fields. By enhancing computational capabilities and addressing challenging data analysis problems, these approaches have the potential to revolutionise a number of domains by leveraging the special properties of quantum systems, such as superposition and entanglement. In this article, we give a thorough analysis of QML and QDL algorithms, discussing their underlying ideas, benefits over conventional models, and potential uses. We talk about the difficulties these algorithms face, such as noisy quantum hardware, constrained qubit resources, training and optimisation issues, input representation and data encoding problems, and limited interpretability. Additionally, we look at current developments and lines of inquiry that aim to address these issues, including hybrid classical-quantum methods, error-mitigating techniques, better data representation techniques, quantum transfer learning, and resource optimisation.
In the ever-evolving landscape of technology, the synthesis of quantum computing with deep learning indicates a new era of advancements. This combination is not just a step but a quantum leap, reshaping the standards of computational abilities and learning algorithms. Quantum computing, known for its ability to handle complex calculations at extraordinary speeds, offers a rich ground for deep learning techniques to grow. Deep learning, a subset of machine learning encouraged by the structure and function of the brain, has already transformed various industries with its ability to learn from vast amounts of data. However, traditional computing systems have their limitations in processing this data. Quantum computing, with its ability, performs multiple calculations simultaneously and solves problems that are currently difficult for classical computers. This chapter explores into the revolutionary impact of quantum computing on deep learning, exploring how it's not just enhancing existing algorithms but also paving the way for new forms of artificial intelligence (AI), its challenges, opportunities and applications.
Quantum Machine Learning (QML) and Quantum Deep Learning (QDL) are emerging research areas that combine quantum computing with machine learning and deep learning techniques.These approaches aim to improve computational speed, feature representation, optimisation, and learning performance in complex data-driven problems.This review paper presents a structured discussion of recent developments in QML and QDL, focusing on major algorithms, application areas, implementation approaches, current limitations, and future research directions.The review covers important quantum-based learning methods, including quantum support vector machines, quantum k-nearest neighbours, quantum principal component analysis, quantum neural networks, variational quantum classifiers, quantum convolutional neural networks, quantum reinforcement learning, quantum generative models, and hybrid quantum-classical models.The paper also discusses how these methods are being explored in healthcare, finance, cybersecurity, communication networks, image processing, natural language processing, and optimisation.However, the review shows that most QML and QDL models are still in the early stages of research.Current challenges include noisy quantum hardware, limited qubit count, data encoding issues, circuit design problems, difficulties with model training, and a lack of standardised benchmarks.The review concludes that QML and QDL have strong future potential, but their practical success depends on improved hardware, stronger algorithms, reliable frameworks, and fair comparison with classical models.
Abstract Quantum computing has immense potential to advance the field of Artificial Intelligence (AI), promising faster processing, better optimization, and the ability to handle complex data structures more effectively. However, several challenges need to be addressed, including hardware limitations, algorithm development, and integration with existing AI workflows. This work provides a comprehensive overview of the current state of quantum AI research, covering key areas such as quantum machine learning, quantum deep learning, quantum natural language processing, quantum problem solving, quantum fuzzy logic, quantum evolutionary computation, and quantum decision making. It also presents a collection of interesting use cases targeted at different vertical sectors. The research methodology involves a thorough literature review to capture the most impactful developments from the standpoint of algorithm scalability in the 2020–2024 timeframe. The review highlights the benefits of quantum approaches, such as improved learning capacity, robustness to overfitting and, in some specific cases, quantum speedups for solving various problems. The interdisciplinary research landscape demonstrates a discernible trajectory toward the implementation of practical, large‐scale quantum computational intelligence. This progression is contingent upon the adoption of scalable algorithms that can deliver a quantum advantage, diverging from the replication of classical AI workflows.
Quantum computing as emerging technology raises high expectations to accelerate complicated calculations, superseding capabilities of presently available supercomputers and to provide a solution to the ever-increasing challenge of processing big amount of data. Besides theoretical promises, quantum computers have successfully been realized in experimental settings during the past few years, and can now also be accessed commercially. Given this availability of quantum computers, their application potential can be scrutinized. Promised to lead to disruptive changes in addressing and solving problems from the domains of simulation, optimization and machine learning, quantum computers have been researched in context of many application scenarios in industrial and academic setting in the recent years. However, it turns out that currently available quantum computers are still to small in size and limited in quality to fulfill the high hopes set in them at the moment. This review reflects on the current state of the technology coming from the perspective of the application, and indicates the developments required on hardware, software and algorithmic side to eventually make quantum computing beneficial for applications.
In the global field of computational exploration, quantum computing stands as an enigmatic force, poised to transcend classical limitations in certain tasks. Recent years have borne witness to an unprecedented surge in quantum computing research, fostering a vibrant ecosystem of inquiry and innovation. This study delves into the complex fabric of quantum computing, revealing its evolution from theoretical inception to blooming practical applications. It defines the evolution of quantum computing and paves a roadmap for its potential future through a detailed examination of cutting-edge research initiatives. This study takes readers on a tour through the history of quantum computing, revealing the rich fabric of technological advances and theoretical achievements. From the early stages of quantum hardware creation to the refinement of quantum algorithms and error mitigation measures, the story unfolds with utmost clarity. Furthermore, it deciphers the mysterious mechanics of found quantum algorithms, contrasting their abilities with regular computational paradigms. This paper aims to cast light on a largely untrod territory of interdisciplinary cooperation: the intersection of quantum computing and deep learning. It paves the way to a new generation of computational advantage through the incorporation of quantum rules and deep learning platforms where its application can redefine model enhancement, algorithms formulation and data modeling. Concepts such as quantum neural networks and quantum-enhanced optimization, paint the picture of potentials yet to be imagined inside this mutually beneficial relationship. Moving from conjecture consideration, this paper aims to discuss how quantum computing supports deep learning concretely in numerous application areas. Deep learning utilizing quantum computing has the possibility of transcending the previous boundaries in such areas as image identification, drug design and additional optimization. This investigation maps out the way forward for pragmatic realization by deconstructing existing applications and speculatively constructing new ones. At the end of the voyage, the paper looks forward and presents a roadmap to the next phase of voyaging and inventing. The requirement for a cross-disciplinary approach is then presented, and quantum computing scientists and deep learners are encouraged to collaborate. Through this process of collaboration and creativity the unison of quantum computers with deep learning represents the pursuit of further boundaryless violations as the next step to the advancement of tremendously significant scientific accomplishment in the future.