Abstract
In this chapter, we explore two approaches that link quantum machine learning to the Ising model. First, we will look at probabilistic models that emerge when we take a Boltzmann machine and add quantum dynamics to the underlying physical system. We then discuss proposals for quantum machine learning with quantum annealers, which are devices that solve optimisation problems with an Ising-type energy function.
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Schuld, M., Petruccione, F. (2021). Approaches Based on the Ising Model. In: Machine Learning with Quantum Computers. Quantum Science and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-83098-4_8
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DOI: https://doi.org/10.1007/978-3-030-83098-4_8
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