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Chapman & Hall/CRC eBooks

Machine Learning with Unsupervised Quantum Models

Prianka Ramachandran Radhabai, Sathya Karunanidhi, Shreyanth Srikanth

Published Oct 1, 2024• 0 citations
Publisher

Abstract

Machine Learning (ML) is the process of training machines to analyze and learn from provided data. It can be categorized into three main types: supervised, unsupervised, and reinforcement learning. Unsupervised learning, a method that allows the discovery of underlying patterns in data without the need for additional information or labeled targets, uncovers hidden patterns within the given dataset but lacks associated labels or classes. Unsupervised techniques prove particularly useful for exploring data and comprehending complex behaviors that challenge human identification within large datasets. They find applications in various fields, such as text categorization (e.g., news articles), anomaly detection, satellite and spatial image processing, medical image analysis, customer segmentation, and recommendation engines. Unsupervised learning primarily serves three main tasks: clustering, association, and dimensionality reduction. Clustering is a technique that automatically groups similar samples together based on their inherent characteristics. Association is a technique used to discover relationships between different features within a dataset. Dimensionality reduction is a technique employed to decrease the number of features in a dataset, especially when dealing with high-dimensional data.

DOI
10.1201/9781003429654-6

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Machine Learning (ML) is the process of training machines to analyze and learn from provided data. It can be categorized into three main types: supervised, unsupervised, and reinforcement learning. Unsupervised learning, a method that allows the discovery of underlying patterns in data without the need for additional information or labeled targets, uncovers hidden patterns within the given dataset but lacks associated labels or classes. Unsupervised techniques prove particularly useful for exploring data and comprehending complex behaviors that challenge human identification within large datasets. They find applications in various fields, such as text categorization (e.g., news articles), anomaly detection, satellite and spatial image processing, medical image analysis, customer segmentation, and recommendation engines. Unsupervised learning primarily serves three main tasks: clustering, association, and dimensionality reduction. Clustering is a technique that automatically groups similar samples together based on their inherent characteristics. Association is a technique used to discover relationships between different features within a dataset. Dimensionality reduction is a technique employed to decrease the number of features in a dataset, especially when dealing with high-dimensional data.
[EN] In a world in which accessible information grows exponentially, the selection of the appropriate information turns out to be an extremely relevant problem. In this context, the idea of Machine Learning (ML), a subfield of Artificial Intelligence, emerged to face problems in data mining, pattern recognition, automatic prediction, among others. Quantum Machine Learning is an interdisciplinary research area combining quantum mechanics with methods of ML, in which quantum properties allow for an exponential speed-up in the algorithms. This field is currently one of the main research areas in companies like Google and Microsoft, due to the revolution that they could provide in data management.
Bias triangles represent features in stability diagrams of Quantum Dot (QD) devices, whose occurrence and property analysis are crucial indicators for spin physics. Nevertheless, challenges associated with quality and availability of data as well as the subtlety of physical phenomena of interest have hindered an automatic and bespoke analysis framework, often still relying (in part) on human labelling and verification. We introduce a feature extraction framework for bias triangles, built from unsupervised, segmentation-based computer vision methods, which facilitates the direct identification and quantification of physical properties of the former. Thereby, the need for human input or large training datasets to inform supervised learning approaches is circumvented, while additionally enabling the automation of pixelwise shape and feature labeling. In particular, we demonstrate that Pauli Spin Blockade (PSB) detection can be conducted effectively, efficiently and without any training data as a direct result of this approach.
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Quantum computing and quantum machines continue to raise awareness once people realized that “Nature isn’t classical. If you want to make a simulation of nature, you’d better make it quantum mechanical.” ( Feynman, 1982 ). On the other hand, as the Internet brings big data, machine learning has become a logical step for researchers looking for tasks such as the patterns behind the data, or for companies seeking to maximize their data’s potential value. The combination of the quantum computing and machine learning (ML) fields has resulted in quantum machine learning (QML), one of today’s most active fields. This preview provides essential concepts in quantum computation, including the basic quantum physics and fundamentals of quantum computing. The next section examines quantum-enhanced ML models and algorithms. The current state of knowledge of applications is discussed in the following section and the preview concludes with open questions for future research on QML.
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.
Quantum Machine Learning investigates the pos-sibility of quantum computers enhancing Machine Learning algorithms. Anomaly segmentation is a fundamental task in various domains to identify irregularities at sample level and can be addressed with both supervised and unsupervised methods. Autoencoders are commonly used in unsupervised tasks, where models are trained to reconstruct normal instances efficiently, al-lowing anomaly identification through high reconstruction errors. While quantum autoencoders have been proposed in the literature, their application to anomaly segmentation tasks remains unexplored. In this paper, we introduce a patch-based quantum autoencoder (QPB-AE) for image anomaly segmentation, with a number of parameters scaling logarithmically with patch size. QPB-AE reconstructs the quantum state of the embedded input patches, computing an anomaly map directly from measurement through a SWAP test without reconstructing the input image. We evaluate its performance across multiple datasets and parameter configurations and compare it against a classical counterpart.