Over the last couple of decades, there has been a significant evolution in machine learning (ML) and it has gained much popularity, as the volume of data grows exponentially and the adoption of ML algorithms brings new perspectives, trends, and patterns on the data which help in the process of decision making in businesses. Machine learning has transformed the way technical experts work; on the other hand, quantum computing is also becoming prevalent: a fast-growing technology that takes advantage of the laws of quantum mechanics to solve large problems which might be too complex for a classical computer to solve. Tech giants like IBM and Google have already built the first wave of quantum computers. Many research organizations, governments and universities are investing funds and researching in quantum computing, as quantum computers are expected to bring revolutionary changes and accelerate outcomes in various fields such as drug discovery, electronic materials, fertilizers, artificial intelligence (AI), and so on. A new initiative for research into the potential of quantum computers to expedite the learning process of ML algorithms has been inspired by different applications of machine learning and the possibilities of quantum computing. Multiple factors exist in the environment which can affect the performance and accuracy of quantum computers. The Earth’s magnetic field can have an impact on quantum computers since they are susceptible to noise, such as Wi-Fi or mobile phone radiation. Likewise, weak machine learning algorithms can produce inaccurate results; their predictions similar to mere human guessing. Applying machine learning algorithms in quantum computers achieves speedup in the learning process. A weak inaccurate algorithm can be improved by an accurate machine learning algorithm accompanied by boosting. The interplay or trade-off between noisy quantum computers and quantum machine learning algorithms is an interesting research field to explore. For example, assume A is a quantum machine learning (QML) algorithm with a theoretically excellent performance. However, A’s performance is poor when used with a noisy quantum computer; this is because only slightly more than half of the inputs result in A’s output being correct. Can we make A perform better, so that its output is correct on two-thirds of the inputs? Boosting algorithms work on this aspect.
Over the last couple of decades, there has been a significant evolution in machine learning (ML) and it has gained much popularity, as the volume of data grows exponentially and the adoption of ML algorithms brings new perspectives, trends, and patterns on the data which help in the process of decision making in businesses. Machine learning has transformed the way technical experts work; on the other hand, quantum computing is also becoming prevalent: a fast-growing technology that takes advantage of the laws of quantum mechanics to solve large problems which might be too complex for a classical computer to solve. Tech giants like IBM and Google have already built the first wave of quantum computers. Many research organizations, governments and universities are investing funds and researching in quantum computing, as quantum computers are expected to bring revolutionary changes and accelerate outcomes in various fields such as drug discovery, electronic materials, fertilizers, artificial intelligence (AI), and so on. A new initiative for research into the potential of quantum computers to expedite the learning process of ML algorithms has been inspired by different applications of machine learning and the possibilities of quantum computing. Multiple factors exist in the environment which can affect the performance and accuracy of quantum computers. The Earth’s magnetic field can have an impact on quantum computers since they are susceptible to noise, such as Wi-Fi or mobile phone radiation. Likewise, weak machine learning algorithms can produce inaccurate results; their predictions similar to mere human guessing. Applying machine learning algorithms in quantum computers achieves speedup in the learning process. A weak inaccurate algorithm can be improved by an accurate machine learning algorithm accompanied by boosting. The interplay or trade-off between noisy quantum computers and quantum machine learning algorithms is an interesting research field to explore. For example, assume A is a quantum machine learning (QML) algorithm with a theoretically excellent performance. However, A’s performance is poor when used with a noisy quantum computer; this is because only slightly more than half of the inputs result in A’s output being correct. Can we make A perform better, so that its output is correct on two-thirds of the inputs? Boosting algorithms work on this aspect.
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.
In recent years, Machine Learning (ML) has started to be ubiquitously applied to practically most of the human activity domains. Although traditional, or classical machine learning (CML) approaches are useful in solving many complex tasks, there are still many challenges that such approaches are facing. One issue is the limitation in the processing speed of current silicon technology based computers, which made researchers look to the underlying quantum theory principles and try to run complex quantum computing experiments. In future, in order to develop more complex artificial intelligence systems, we will have to process huge amounts of data at high speed, and the existing classical computing will probably not serve well this purpose, due to silicon technology limitations. A different technology that can handle huge volumes of data and at high speed is needed. In recent years, the progress in quantum computing research seems to provide hopeful answers to overcome the speed processing barrier. This is very important for the training of many computational intensive machine learning models. The latest advancements in quantum technology appear to be promising, which can boost the field of machine learning overall. In this chapter, we discuss the transition from classical machine learning to quantum machine learning (QML) and explore the recent progress in this domain. QML is not only associated with the development of high-performance machine learning algorithms that can run on a quantum computer with significant performance improvements but also has a very diverse meaning in other aspects. The chapter tried to touch those aspects in brief too, but the main focus is on the advancements in the field of developing machine learning algorithms that will run on a quantum computer.
This tutorial provides an overview of Quantum Machine Learning (QML), a relatively novel discipline that brings together concepts from Machine Learning (ML), Quantum Computing (QC) and Quantum Information (QI). The great development experienced by QC, partly due to the involvement of giant technological companies as well as the popularity and success of ML have been responsible of making QML one of the main streams for researchers working on fuzzy borders between Physics, Mathematics and Computer Science. A possible, although arguably coarse, classification of QML methods may be based on those approaches that make use of ML in a quantum experimentation environment and those others that take advantage of QC and QI to find out alternative and enhanced solutions to problems driven by data, oftentimes offering a considerable speedup and improved performances as a result of tackling problems from a complete different standpoint. Several examples will be provided to illustrate both classes of methods.
Quantum Machine Learning (QML) is the emerging confluence of quantum computing and artificial intelligence that promises to solve computational problems inaccessible to classical systems. Using quantum principles such as superposition, entanglement, and interference, QML promises exponential speed-ups and new paradigms for data processing in machine learning tasks. This review gives an overview of QML, from advancements in quantum-enhanced classical ML to native quantum algorithms and hybrid quantum-classical frameworks. It varies from applications in optimization, drug discovery, and quantum-secured communications, showcasing how QML can change healthcare, finance, and logistics industries. Even though this approach holds so much promise, significant challenges remain to be addressed-noisy qubits, error correction, and limitations in data encoding-that must be overcome by interdisciplinary research soon. The paper tries to collate the state of the art of QML in theoretical underpinnings, practical applications, and directions into the future.
Quantum Machine Learning (QML) is a new phraseology in the world today. With various upcoming Artificial Intelligence (AI) technologies, everyday data scientists and corporate around the world are trying to harness its power. QML borrows concepts of Quantum Computing and QML algorithms enhance the existing Machine Learning (ML) algorithms by processing datasets more efficiently. Quantum Computers are a powering force driving QML algorithms that boosts computational power and helps analyse data. QML algorithms can be applied in sectors involving number crunching, pattern identification and so on. In the existing world, QML can be a game-changer for organizations that work on identifying product correlations and customer behaviour in a brief time. This paper discusses studies related to principles of Quantum Mechanics, Quantum Computing and QML algorithm. Additionally, it explores opportunities and challenges faced in the field of QML. Lastly, the paper talks about QML applications and compares various available QML toolkits from the market. Future direction and exhaustive survey of papers from various spectrums related to Quantum Computing is presented in this paper.
Quantum computing and machine learning (QML) have emerged as a revolutionary per approach to solve complex computational problems that classical systems are unable to. The use of superposition, entanglement, and quantum parallelism helps analyze data up to ten times faster than in a non-quantum manner, enhancing optimization, pattern identification, and computational efficiency. This paper studies the potential of quantum algorithms and machine learning in areas of categorization, grouping, and regression in banking, healthcare, cybersecurity, and scientific simulations. QML shines when it comes to generalizing low cost over high-dimensional data spaces. State-of-theart quantum-enhanced algorithms (QSVMs, VQCs, QBMs) yield better solutions to complex pattern recognition problems. Quantum-inspired neural networks accelerate deep learning architectures by optimizing gradient descent and decreasing training periods. The following research explores how QML enables a potentially computationally efficient solution to combinatorial optimization problems in the realms of logistics, cryptography, and artificial intelligence through quantum annealing and hybrid quantumclassical models. QML is still not able to deploy for the hardware limitations, noise sensitive nature, and quantum decoherence, despite promising results. The study underscores the importance of error correction procedures, as well as noise-resilient quantum circuits, and ultimately their scaling to full computers to reconcile theoretical promise with real-world application. It explores the ability of hybrid quantum-classical models to provide near-term developed applications as a bridge to realizable quantum computation. In this article, researchers present a thorough overview of quantum algorithms, reduced computational complexity, and practical applications, furthering the field of quantumenhanced machine learning. Industries might be revolutionized with quantum computers and AI-based decision-making systems working together with unprecedented computational power. In particular, quantum algorithms, hardware, and quantum cloud computing platforms must be a research focus area to ensure that quantum will deliver the most significant advances in AI.