Quantum Machine Learning in Climate Change and Sustainability: a Review

While quantum mechanics, while classical computation is built classical machine learning techniques have been applied on the rules of classical physics (Nielsen and Chuang to several problems in this area, Quantum machine learning 2010)(Desurvire 2009). Classical computers operate by manipulating (QML) offers a promising approach to overcome classical bits, while in quantum computers, the information machine learning (ML) limitations in climate change is processed via the means of its building blocks called research by leveraging quantum computing (Singh et al. qubits. Quantum bits or qubits live in a two-dimensional linear 2021). This section introduces the need for significant actions vector or Hilbert space, unlike bits that can assume discrete to face climate change, most importantly, by introducing values of either 0 or 1. The two computational basis new cutting-edge technologies such as quantum machine states that span the Hilbert space of a qubit are denoted by learning (QML) (Wittek 2014) to help accelerate the CO2-the states |0 and |1, as shown in Eq. (1).