Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

IGI Global eBooks

Quantum-Computing-Inspired Algorithms in Machine Learning

Deeksha Kaul, Harika Raju, Bala Krushna Tripathy

Published Nov 27, 2020• 3 citations
Publisher

Abstract

In this chapter, the authors discuss the use of quantum computing concepts to optimize the decision-making capability of classical machine learning algorithms. Machine learning, a subfield of artificial intelligence, implements various techniques to train a computer to learn and adapt to various real-time tasks. With the volume of data exponentially increasing, solving the same problems using classical algorithms becomes more tedious and time consuming. Quantum computing has varied applications in many areas of computer science. One such area which has been transformed a lot through the introduction of quantum computing is machine learning. Quantum computing, with its ability to perform tasks in logarithmic time, aids in overcoming the limitations of classical machine learning algorithms.

DOI
10.4018/978-1-7998-7705-9.ch021

Related papers

Quantum-Computing-Inspired Algorithms in Machine Learning

Deeksha Kaul, Harika Raju, Bala Krushna Tripathy

In this chapter, the authors discuss the use of quantum computing concepts to optimize the decision-making capability of classical machine learning algorithms. Machine learning, a subfield of artificial intelligence, implements various techniques to train a computer to learn and adapt to various real-time tasks. With the volume of data exponentially increasing, solving the same problems using classical algorithms becomes more tedious and time consuming. Quantum computing has varied applications in many areas of computer science. One such area which has been transformed a lot through the introduction of quantum computing is machine learning. Quantum computing, with its ability to perform tasks in logarithmic time, aids in overcoming the limitations of classical machine learning algorithms.

Advances in computer and electrical engineering book series•Jan 1, 2018•5 citations

Quantum Artificial Intelligence: Enhancing Machine Learning with Quantum Computing

Aarav Lohia

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.

Journal of Quantum Science and Technology.•Jul 2, 2024•6 citations

A Comprehensive Survey on Quantum Machine Learning and Possible Applications

Muhammad Junaid Umer, Muhammad Imran Sharif

Machine learning is a branch of artificial intelligence that is being used at a large scale to solve science, engineering, and medical tasks. Quantum computing is an emerging technology that has a very high computational ability to solve complex problems. Classical machine learning with traditional systems has some limitations for problem-solving due to a large amount of data availability. Quantum machine learning has high performance and computational ability that can effectively be used to solve computation tasks. This study reviews the latest articles in quantum computing and quantum machine learning. Building blocks of quantum computing and different flavors of quantum algorithms are also discussed. The latest work in quantum neural networks is also presented. In the end, different possible applications of quantum computing are also discussed.

International Journal of E-Health and Medical Communications•Dec 23, 2022•34 citations

Quantum machine learning-using quantum computation in artificial intelligence and deep neural networks: Quantum computation and machine learning in artificial intelligence

Sayantan Gupta, Subhrodip Mohanta, Mayukh Chakraborty +1 more

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.

International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON)•Aug 1, 2017•31 citations

3. Quantum optimization for machine learning

Ashish Mani, Siddhartha Bhattacharyya, Amlan Chatterjee

Machine learning is a branch of Artificial Intelligence that seeks to make machines learn from data. It is being applied for solving real world problems with huge amount of data. Though, Machine Learning is receiving wide acceptance, however, execution time is one of the major concerns in practical implementations of Machine Learning techniques. It largely comprises of a set of techniques that trains a model by reducing the error between the desired or actual outcome and an estimated or predicted outcome, which is often called as loss function. Thus, training in machine learning techniques often requires solving a difficult optimization problem, which is the most expensive step in the entire model-building process and its applications. One of the possible solutions in near future for reducing execution time of training process in Machine learning techniques is to implement them on quantum computers instead of classical computers. It is conjectured that quantum computers may be exponentially faster than classical computers for solving problems which involve matrix operations. Some of the machine learning techniques like support vector machines make extensive use of matrices, which can be made faster by implementing them on quantum computers. However, their efficient implementation is non-trivial and requires existence of quantum memories. Thus, another possible solution in near term is to use a hybrid of Classical Quantum approach, where a machine learning model is implemented in classical computer but the optimization of loss function during training is performed on quantum computer instead of classical computer. Several Quantum optimization algorithms have been proposed in recent years, which can be classified as gradient based and gradient free optimization techniques. Gradient based techniques require the nature of optimization problem being solved to be convex, continuous and differentiable otherwise if the problem is non-convex then they can find local optima only whereas gradient free optimization techniques work well even with non-continuous, non-linear and nonconvex optimization problems. This chapter discusses a global optimization technique based on Adiabatic Quantum Computation (AQC) to solve minimization of loss function without any restriction on its structure and the underlying model, which is being learned. Further, it is also shown that in the proposed framework, AQC based approach would be superior to circuit-based approach in solving global optimization problems.

De Gruyter eBooks•Jun 8, 2020•0 citations

Quantum Machine Learning: Scope for real-world problems

Abhishek Jadhav, Akhtar Rasool, Manasi Gyanchandani

Quantum computing with its inherent parallelism provides a quantum advantage over classical computing. Its potential to offer breakthrough advances in various areas of science and engineering is foreseen. Machine learning is one of the key areas where the power of quantum computing can be utilized. Though many machine learning algorithms have been successfully developed to solve a variety of problems in the past decades, these algorithms take a long time to train. Also working on today's colossal datasets makes these algorithms computationally intensive. Quantum machine learning by utilizing the concepts of superposition and entanglement promises a solution to this problem. Quantum machine learning algorithms are in surface for the past few years and majority of the current research has dealt with the two machine learning problems namely classification and clustering. In this paper, a brief review of the recent techniques and algorithms of quantum machine learning and its scope in solving real world problems is studied.

Procedia Computer Science•Jan 1, 2023•67 citations
In this chapter, the authors discuss the use of quantum computing concepts to optimize the decision-making capability of classical machine learning algorithms. Machine learning, a subfield of artificial intelligence, implements various techniques to train a computer to learn and adapt to various real-time tasks. With the volume of data exponentially increasing, solving the same problems using classical algorithms becomes more tedious and time consuming. Quantum computing has varied applications in many areas of computer science. One such area which has been transformed a lot through the introduction of quantum computing is machine learning. Quantum computing, with its ability to perform tasks in logarithmic time, aids in overcoming the limitations of classical machine learning algorithms.
In this chapter, the authors discuss the use of quantum computing concepts to optimize the decision-making capability of classical machine learning algorithms. Machine learning, a subfield of artificial intelligence, implements various techniques to train a computer to learn and adapt to various real-time tasks. With the volume of data exponentially increasing, solving the same problems using classical algorithms becomes more tedious and time consuming. Quantum computing has varied applications in many areas of computer science. One such area which has been transformed a lot through the introduction of quantum computing is machine learning. Quantum computing, with its ability to perform tasks in logarithmic time, aids in overcoming the limitations of classical machine learning algorithms.
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.
Machine learning is a branch of artificial intelligence that is being used at a large scale to solve science, engineering, and medical tasks. Quantum computing is an emerging technology that has a very high computational ability to solve complex problems. Classical machine learning with traditional systems has some limitations for problem-solving due to a large amount of data availability. Quantum machine learning has high performance and computational ability that can effectively be used to solve computation tasks. This study reviews the latest articles in quantum computing and quantum machine learning. Building blocks of quantum computing and different flavors of quantum algorithms are also discussed. The latest work in quantum neural networks is also presented. In the end, different possible applications of quantum computing are also discussed.
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 is a branch of Artificial Intelligence that seeks to make machines learn from data. It is being applied for solving real world problems with huge amount of data. Though, Machine Learning is receiving wide acceptance, however, execution time is one of the major concerns in practical implementations of Machine Learning techniques. It largely comprises of a set of techniques that trains a model by reducing the error between the desired or actual outcome and an estimated or predicted outcome, which is often called as loss function. Thus, training in machine learning techniques often requires solving a difficult optimization problem, which is the most expensive step in the entire model-building process and its applications. One of the possible solutions in near future for reducing execution time of training process in Machine learning techniques is to implement them on quantum computers instead of classical computers. It is conjectured that quantum computers may be exponentially faster than classical computers for solving problems which involve matrix operations. Some of the machine learning techniques like support vector machines make extensive use of matrices, which can be made faster by implementing them on quantum computers. However, their efficient implementation is non-trivial and requires existence of quantum memories. Thus, another possible solution in near term is to use a hybrid of Classical Quantum approach, where a machine learning model is implemented in classical computer but the optimization of loss function during training is performed on quantum computer instead of classical computer. Several Quantum optimization algorithms have been proposed in recent years, which can be classified as gradient based and gradient free optimization techniques. Gradient based techniques require the nature of optimization problem being solved to be convex, continuous and differentiable otherwise if the problem is non-convex then they can find local optima only whereas gradient free optimization techniques work well even with non-continuous, non-linear and nonconvex optimization problems. This chapter discusses a global optimization technique based on Adiabatic Quantum Computation (AQC) to solve minimization of loss function without any restriction on its structure and the underlying model, which is being learned. Further, it is also shown that in the proposed framework, AQC based approach would be superior to circuit-based approach in solving global optimization problems.
Quantum computing with its inherent parallelism provides a quantum advantage over classical computing. Its potential to offer breakthrough advances in various areas of science and engineering is foreseen. Machine learning is one of the key areas where the power of quantum computing can be utilized. Though many machine learning algorithms have been successfully developed to solve a variety of problems in the past decades, these algorithms take a long time to train. Also working on today's colossal datasets makes these algorithms computationally intensive. Quantum machine learning by utilizing the concepts of superposition and entanglement promises a solution to this problem. Quantum machine learning algorithms are in surface for the past few years and majority of the current research has dealt with the two machine learning problems namely classification and clustering. In this paper, a brief review of the recent techniques and algorithms of quantum machine learning and its scope in solving real world problems is studied.