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II Applications to Physics
Chapter 10: Spinglass and Neural Networks To begin with, neural networks are
based on neural circuits formed by neurons in human brains. One of the important
mechanisms of the brain is memory. The Hopfield model, which explains the
mechanism of memory in terms of physics, is a bridge between physics and
neural networks. In this chapter, we explain the Hopfield model and investigate
the relationship between machine learning and spin glass, which is still a rich
subject in condensed matter physics.
Chapter 11: Quantum Manybody Systems, Tensor Networks and Neural
Networks In condensed matter physics, finding the wave function of a quantum
many-body system is the most important issue. Theoretical development in
recent years includes a wave function approximation using a tensor network.
At first glance, it is very similar to neural network diagrams, and how are they
actually related? In this chapter we will see the relation and mapping, and that
the restricted Boltzmann machine is closely related to tensor networks.
Chapter 12: Application to Superstring Theory The last chapter describes an
example of solving the inverse problem of string theory as an application of
deep learning. The superstring theory unifies gravity and other forces, and in
recent years, the “holographic principle,” that the world governed by gravity is
equivalent to the world of other forces, has been actively studied. We will solve
the inverse problem of the emergence of the gravitational world by applying the
correspondence to the dynamical system seen in Chap. 9, and look at the new
relationship between machine learning and spacetime.
These chapters, from Chaps. 7 to 12, can be read almost independently. You can
pick up a section that interests you. Reading all the chapters will lead the readers to
find a clear understanding of the relationship between machine learning and physics
through its various examples and uses in physics, and through its history.
II Applications to Physics
Chapter 10: Spinglass and Neural Networks To begin with, neural networks are
based on neural circuits formed by neurons in human brains. One of the important
mechanisms of the brain is memory. The Hopfield model, which explains the
mechanism of memory in terms of physics, is a bridge between physics and
neural networks. In this chapter, we explain the Hopfield model and investigate
the relationship between machine learning and spin glass, which is still a rich
subject in condensed matter physics.
Chapter 11: Quantum Manybody Systems, Tensor Networks and Neural
Networks In condensed matter physics, finding the wave function of a quantum
many-body system is the most important issue. Theoretical development in
recent years includes a wave function approximation using a tensor network.
At first glance, it is very similar to neural network diagrams, and how are they
actually related? In this chapter we will see the relation and mapping, and that
the restricted Boltzmann machine is closely related to tensor networks.
Chapter 12: Application to Superstring Theory The last chapter describes an
example of solving the inverse problem of string theory as an application of
deep learning. The superstring theory unifies gravity and other forces, and in
recent years, the “holographic principle,” that the world governed by gravity is
equivalent to the world of other forces, has been actively studied. We will solve
the inverse problem of the emergence of the gravitational world by applying the
correspondence to the dynamical system seen in Chap. 9, and look at the new
relationship between machine learning and spacetime.
These chapters, from Chaps. 7 to 12, can be read almost independently. You can
pick up a section that interests you. Reading all the chapters will lead the readers to
find a clear understanding of the relationship between machine learning and physics
through its various examples and uses in physics, and through its history.