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Quantum machine learning is a subject in the making, with endless possibilities for applications in the near and long term. Nonetheless, to find out what quantum machine learning has to offer its numerous possible avenues first have to be explored by an interdisciplinary community of scientists and quantum computing enthusiasts. This book in intended to be a starting point for this journey, as it to introduces key concepts, ideas, and algorithms that are the result of the first few years of quantum machine learning research. The aim is to provide a comprehensive literature review and to summarize key topics that appear often in quantum machine learning, to put them into context and make them accessible to a broader audience in order to foster future research and applications.
Key Features:
- An associated Github repository with example code implementations
- A chapter on quantum generative models.
- Accessible reference text useful for both students and researchers.
- A discussion of implementation on different NISQ platforms (squeezed light modes vs trapped ions vs superconducting qubits) and the associated challenges
- Case studies
1. Introduction: This chapter will provide the reader with an overview of the field, starting with a gentle introduction to machine learning, the promise of quantum computing, and the perspective for near-term devices.
2. Quantum Information: A high-level introduction to quantum theory, the postulates of quantum mechanics, quantum computing, and information encoding. An overview of some of the most relevant quantum computing algorithms like the Deutsch-Josza and Grover algorithm will also be provided.
3. Information Encoding: An overview of the current methods for information encoding, like amplitude and Hamiltonian encoding.
4. Quantum computing for inference: On this chapter linear and kernel models will be introduced in the context of quantum information.
5. Quantum Variational Optimization: This chapter will describe variational algorithms. Variational algorithms are physics-inspired algorithms that seek to find the minimum energy eigenstate of a given system through variational methods. This chapter will cover some examples, like the variational eigensolver and the quantum approximate optimization algorithm.
6. Variational classifiers and neural networks: This chapter will cover the concept of hybrid training, meaning, the implementation of a learning model on quantum and classical resources. In this context, the variational algorithms introduced in the previous chapter will be trained as quantum models for learning. Backpropagation and the estimation of gradients in the quantum context will also be discussed.
7. Variational Encoders and Quantum Boltzmann machines: In this chapter, we cover variational encoders and Quantum Boltzmann machine algorithms, which can be thought of as the early versions of generative models
8. Quantum Generative Models: One of the most promising areas in quantum machine learning is the area of generative models, which are expected to demonstrate an advantage over their classical counterparts. In this chapter, an overview of generative models will be provided, as well as an overview of the power of expressibility in these models.
9. Avoiding barren plateaus: Quantum learning models often suffer from instabilities in the training process and vanishing gradients. In this chapter, some of the current techniques to avoid some of these issues will be presented.