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Apress eBooks

Quantum Deep Learning

Santanu Pattanayak

Published Jan 1, 2021• 1 citation
Publisher

Abstract

In the past decade, deep learning has had a profound impact on machine learning and artificial intelligence in general. Around the same time, quantum algorithms have proven to be effective in solving some of the intractable problems on classical computers. Quantum computing can provide a much more efficient framework for deep learning than the existing classical regime by providing better optimization of the underlying objective function. The field of quantum deep learning attempts to build neural networks that can benefit from the quantum information flow through the network. In summary, quantum deep learning networks are accompanied by quantum layers consisting of quantum gates.

DOI
10.1007/978-1-4842-6522-2_6

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In the past decade, deep learning has had a profound impact on machine learning and artificial intelligence in general. Around the same time, quantum algorithms have proven to be effective in solving some of the intractable problems on classical computers. Quantum computing can provide a much more efficient framework for deep learning than the existing classical regime by providing better optimization of the underlying objective function. The field of quantum deep learning attempts to build neural networks that can benefit from the quantum information flow through the network. In summary, quantum deep learning networks are accompanied by quantum layers consisting of quantum gates.
In recent years, deep learning has had a profound impact on machine learning and artificial intelligence. At the same time, algorithms for quantum computers have been shown to efficiently solve some problems that are intractable on conventional, classical computers. We show that quantum computing not only reduces the time required to train a deep restricted Boltzmann machine, but also provides a richer and more comprehensive framework for deep learning than classical computing and leads to significant improvements in the optimization of the underlying objective function. Our quantum methods also permit efficient training of full Boltzmann machines and multi-layer, fully connected models and do not have well known classical counterparts.
In recent years, deep learning has had a profound impact on machine learning and artificial intelligence. At the same time, algorithms for quantum computers have been shown to efficiently solve some problems that are intractable on conventional, classical computers. We show that quantum computing not only reduces the time required to train a deep restricted Boltzmann machine, but also provides a richer and more comprehensive framework for deep learning than classical computing and leads to significant improvements in the optimization of the underlying objective function. Our quantum methods also permit efficient training of multilayer and fully connected models.
Quantum computing is described as a process by which a system calculates output. Quantum physics usually refers to the smallest discrete unit of any property; the basic unit of data in quantum is the qubit; this qubit unit is equivalent to the bit unit in classical neural networks. Quantum deep learning combines quantum computing with deep learning to reduce training time for neural networks, which has proven effective in solving some intractable problems on classical computers. Quantum deep learning has proven effective in solving some intractable problems on classical computers. A quantum network can benefit from quantum informationflow because it is a more efficient framework than classical systems. Each quantum deep learning consists of a quantum gate. In this review, we provide a comprehensive review of recent studies that include different quantum deep learning applications, including healthcare, handwriting, and many others. Also, methodologies, problems, main datasets, results, strengths, limitations, and challenges are included in this review.
Deep quantum learning (DQL) is an emerging interdisciplinary field that bridges the realms of quantum computing and deep learning. By integrating the principles of quantum mechanics into deep neural networks, DQL seeks to harness the unique capabilities of quantum systems to revolutionize the landscape of machine learning and artificial intelligence. Quantum neural networks (QNNs) lie at the heart of DQL, utilizing quantum gates and entanglement to process classical data in quantum states. Quantum embeddings and quantum data preprocessing techniques are employed to encode classical information into quantum probability distributions, enabling quantum algorithms to manipulate classical data efficiently. Furthermore, hybrid quantum-classical optimization methods facilitate the training of QNNs by combining classical optimization techniques with quantum circuits. DQL shows great promise in solving computationally complex problems and may drive advancements in various fields, such as chemistry, optimization, and pattern recognition.
This paper explores the use of quantum computing for solving machine learning problems more efficiently. Today's quantum computers are rather primitive, so only relatively small machine learning problems can be solved at this time. Nevertheless, the machine learning problems described here that have been solved on quantum simulators or actual quantum computers indicates the potential power of quantum computers for solving computationally intensive machine learning problems such as the deep learning multi-layer neural networks.
In the ever-evolving landscape of technology, the synthesis of quantum computing with deep learning indicates a new era of advancements. This combination is not just a step but a quantum leap, reshaping the standards of computational abilities and learning algorithms. Quantum computing, known for its ability to handle complex calculations at extraordinary speeds, offers a rich ground for deep learning techniques to grow. Deep learning, a subset of machine learning encouraged by the structure and function of the brain, has already transformed various industries with its ability to learn from vast amounts of data. However, traditional computing systems have their limitations in processing this data. Quantum computing, with its ability, performs multiple calculations simultaneously and solves problems that are currently difficult for classical computers. This chapter explores into the revolutionary impact of quantum computing on deep learning, exploring how it's not just enhancing existing algorithms but also paving the way for new forms of artificial intelligence (AI), its challenges, opportunities and applications.