The effect of superposition and
entanglement on hybrid quantum machine learning for weather forecasting (pp0181-0194)
Besir Ogur and Ihsan Yılmaz
doi:
https://doi.org/10.26421/QIC23.3-4-1
Abstracts:
Recently, proposed algorithms for quantum computing and generated
quantum computer technologies continue to evolve. On the other hand,
machine learning has become an essential method for solving many
problems such as computer vision, natural language processing,
prediction and classification. Quantum machine learning is a new field
developed by combining the advantages of these two primary methods. As a
hybrid approach to quantum and classical computing,
variational
quantum circuits are a form of machine learning that allows predicting
an output value against input variables. In this study, the effects of
superposition and entanglement on weather forecasting, were investigated
using a
variational quantum circuit model
when the
dataset
size is small. The use of the entanglement layer between the
variational
layers has made significant improvements on the circuit performance. The
use of the superposition layer before the data encoding layer resulted
in the use of less
variational
layers.
Key Words:
Quantum Computing, Machine
Learning, Weather
Forcasting,
Variational Quantum Circuit, Hybrid
Quantum-Classic Neural Network |