International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON)
Quantum machine learning-using quantum computation in artificial intelligence and deep neural networks: Quantum computation and machine learning in artificial intelligence
Machine Learning or Artificial Intelligence basically involves tasks of modifying and supervising problems taken as vectors in multi-dimensional space. The Primitive algorithms which are used take Polynomial Time for computing such vector problems which are not fruitful for us, on the other hand, Quantum algorithms have the capability to solve such vector problems in a considerable amount of time by using Quantum-Mechanical operations. For example, we can perform a Database Search in a time which is Quadratic-ally faster than the primitive search algorithm. Quantum Algorithms rely on Quantum physics and therefore the algorithms are Incoherent in nature and this property makes them more interesting to study. In this paper, we provide the insights of Quantum Machine Learning and we formally prove that the Execution Time of the algorithm is greatly optimized with the help of Adiabatic Quantum Learning. Also, we prove that Quantum Associative Memories can store exponentially more data than its primitive counterparts. Data mining concept is very similar to Machine Learning and we will also show how QML will be beneficial in such cause as well.
Machine Learning or Artificial Intelligence basically involves tasks of modifying and supervising problems taken as vectors in multi-dimensional space. The Primitive algorithms which are used take Polynomial Time for computing such vector problems which are not fruitful for us, on the other hand, Quantum algorithms have the capability to solve such vector problems in a considerable amount of time by using Quantum-Mechanical operations. For example, we can perform a Database Search in a time which is Quadratic-ally faster than the primitive search algorithm. Quantum Algorithms rely on Quantum physics and therefore the algorithms are Incoherent in nature and this property makes them more interesting to study. In this paper, we provide the insights of Quantum Machine Learning and we formally prove that the Execution Time of the algorithm is greatly optimized with the help of Adiabatic Quantum Learning. Also, we prove that Quantum Associative Memories can store exponentially more data than its primitive counterparts. Data mining concept is very similar to Machine Learning and we will also show how QML will be beneficial in such cause as well.
Quantum computing has emerged as a transformative technology with the potential to revolutionize artificial intelligence (AI) and machine learning (ML). This paper explores the intersection of quantum computing and AI, focusing on how quantum principles can enhance computational capabilities and address challenges in traditional machine learning approaches. Key aspects discussed include quantum algorithms such as quantum support vector machines, quantum neural networks, and quantum variational algorithms, which leverage quantum superposition and entanglement to process vast amounts of data more efficiently than classical counterparts. These algorithms promise to accelerate tasks such as optimization, pattern recognition, and data classification, thereby advancing the capabilities of AI systems. Moreover, quantum computing offers potential breakthroughs in solving combinatorial optimization problems that are computationally intensive for classical computers. Quantum annealing and other quantum optimization techniques are explored for their application in AI, providing novel approaches to solving complex decision-making problems.
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 computing is a rapidly growing discipline that uses properties of quantum mechanics to compute problems that are challenging for traditional computational means. On the other hand, machine learning is a subset of artificial intelligence that enhances a computer's ability to learn patterns from experience. Massive data will get bigger with time; machine learning may sometimes be up to the task of big data, while quantum computing can perform fast computing. The crossbreed of quantum computing and machine learning brought about another field: quantum ML, or quantum machine learning. Applying the quantum learning algorithm includes benefiting from the fast processing in quantum computing and demonstrative speedup over the classical algorithm. Another subfield of artificial intelligence includes natural language processing, through which the computer can understand human languages. The chasers seek to apply quantum machine speedup in natural language processing. In this paper, firstly, we explain how from quantum computing to quantum machine learning. We then discuss the current trends regarding quantum machine learning for natural language processing. We also offer classic and quantum-based long short-term memory for POS tagging of code-mixed language and social media code mixed language. According to the above experiment, it is clear that the quantum-based long short-term memory works better than the classical long short-term memory for POS tagging for code-mixed datasets. Quantum computing is a rapidly developing field that uses the properties of quantum mechanics to solve problems challenging for classical computations.
Quantum machine learning (QML) is an emerging interdisciplinary field that combines the principles of quantum mechanics with machine learning algorithms, offering promising solutions to complex computational problems. This paper provides an extensive introduction to quantum machine learning, exploring the synergies between artificial intelligence and quantum technology. It begins by elucidating the foundational concepts of quantum computing, including quantum bits (qubits), quantum gates, superposition, and entanglement. Subsequently, the paper delves into the basic principles of machine learning and the challenges faced by classical methods in handling large-scale data and solving computationally intensive tasks. With a strong foundation in place, the focus shifts towards the integration of quantum mechanics with machine learning, showcasing the advantages and potential breakthroughs QML offers over classical approaches. The exploration of quantum algorithms for machine learning, such as quantum support vector machines (QSVM), quantum neural networks (QNN), and quantum variational algorithms, reveals the power of quantum-enhanced techniques in data classification, regression, and clustering.
Quantum machine learning (QML) represents a cutting-edge convergence of quantum computing and artificial intelligence, aiming to leverage quantum phenomena such as superposition, entanglement, and quantum parallelism to enhance machine learning algorithms. By exploiting the computational advantages of quantum systems, QML offers the potential for exponential speedups in data processing, optimization, and pattern recognition tasks, particularly for high-dimensional and complex datasets. This article explores the foundational principles, algorithmic approaches, and practical applications of quantum machine learning across domains including finance, chemistry, drug discovery, optimization, and natural language processing. It discusses quantum-enhanced supervised and unsupervised learning, quantum neural networks, variational quantum algorithms, and hybrid quantum-classical architectures. Additionally, the article addresses challenges related to hardware limitations, noise, scalability, and interpretability, as well as the emerging trends in near-term quantum devices, quantum simulators, and integration with classical AI pipelines. Quantum machine learning holds the promise of revolutionizing computational intelligence, offering transformative capabilities for solving problems that are currently intractable for classical systems.
Quantum Computing can be defined as an intersection among Quantum Physics, Computer Science and Mathematics. Though the behavior of quantum mechanics is spooky, the mathematics behind it is simpler and easier than classical one. Using superposition and entanglement, quantum computing can solve complex problems efficiently and it would take less amount of time than its classical counterpart to do so. Machine Learning (ML) is a subset of Artificial Intelligence (AI) which helps to learn from data, analyze patterns and make itself improved. When quantum computing is blended with Machine Learning, Quantum Machine Learning pops up. In this survey, we explore what Quantum Machine Learning (QML) is and how it actually works. We try to demonstrate the very quantum touch on our known machine learning algorithms. We also delve into the various application areas of QML. This paper helps to understand QML from basics to advanced levels.