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Apress eBooks

Quantum Machine Learning: An Applied Approach

Santanu Ganguly

Published Jan 1, 2021• 30 citations
Publisher

Abstract

This book on adapting quantum computing and machine learning algorithms takes a hands-on approach using updated libraries.
DOI
10.1007/978-1-4842-7098-1

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Quantum Machine Learning: An Applied Approach: The Theory and Application of Quantum Machine Learning in Science and Industry

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Although it will be a while before a practical quantum computer is available, there is no need to hold off. Methods and algorithms are being developed to demonstrate the feasibility of running machine learning (ML) pipelines in QC (Quantum Computing). There is a lot of ongoing work on general QML (Quantum Machine Learning) algorithms and applications. However, a working model or pipeline for a text classifier using quantum algorithms isn't available. This paper introduces quantum machine learning w.r.t text classification to readers of classical machine learning. It begins with a brief description of quantum computing and basic quantum algorithms, with an emphasis on building text classification pipelines. A new approach is introduced to implement an end-to-end text classification framework (Quantum Text Classifier - QTC), where pre- and post-processing of data is performed on a classical computer, and text classification is performed using the QML algorithm. This paper also presents an implementation of the QTC framework and available quantum ML algorithms for text classification using the IBM Qiskit library and IBM backends.

arXiv:2305.12783•May 22, 2023•2 citations

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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.

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AutoQML: A Framework for Automated Quantum Machine Learning

Marco Roth, David A. Kreplin, Daniel Basilewitsch +8 more

Automated Machine Learning (AutoML) has significantly advanced the efficiency of ML-focused software development by automating hyperparameter optimization and pipeline construction, reducing the need for manual intervention. Quantum Machine Learning (QML) offers the potential to surpass classical machine learning (ML) capabilities by utilizing quantum computing. However, the complexity of QML presents substantial entry barriers. We introduce AutoQML, a novel framework that adapts the AutoML approach to QML, providing a modular and unified programming interface to facilitate the development of QML pipelines. AutoQML leverages the QML library sQUlearn to support a variety of QML algorithms. The framework is capable of constructing end-to-end pipelines for supervised learning tasks, ensuring accessibility and efficacy. We evaluate AutoQML across four industrial use cases, demonstrating its ability to generate high-performing QML pipelines that are competitive with both classical ML models and manually crafted quantum solutions.

arXiv:2502.21025•IEEE International Conference on Quantum Software (QSW)•Jul 7, 2025•2 citations
Although it will be a while before a practical quantum computer is available, there is no need to hold off. Methods and algorithms are being developed to demonstrate the feasibility of running machine learning (ML) pipelines in QC (Quantum Computing). There is a lot of ongoing work on general QML (Quantum Machine Learning) algorithms and applications. However, a working model or pipeline for a text classifier using quantum algorithms isn't available. This paper introduces quantum machine learning w.r.t text classification to readers of classical machine learning. It begins with a brief description of quantum computing and basic quantum algorithms, with an emphasis on building text classification pipelines. A new approach is introduced to implement an end-to-end text classification framework (Quantum Text Classifier - QTC), where pre- and post-processing of data is performed on a classical computer, and text classification is performed using the QML algorithm. This paper also presents an implementation of the QTC framework and available quantum ML algorithms for text classification using the IBM Qiskit library and IBM backends.
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.
Automated Machine Learning (AutoML) has significantly advanced the efficiency of ML-focused software development by automating hyperparameter optimization and pipeline construction, reducing the need for manual intervention. Quantum Machine Learning (QML) offers the potential to surpass classical machine learning (ML) capabilities by utilizing quantum computing. However, the complexity of QML presents substantial entry barriers. We introduce AutoQML, a novel framework that adapts the AutoML approach to QML, providing a modular and unified programming interface to facilitate the development of QML pipelines. AutoQML leverages the QML library sQUlearn to support a variety of QML algorithms. The framework is capable of constructing end-to-end pipelines for supervised learning tasks, ensuring accessibility and efficacy. We evaluate AutoQML across four industrial use cases, demonstrating its ability to generate high-performing QML pipelines that are competitive with both classical ML models and manually crafted quantum solutions.