  The taxonomic composition and abundance of phytoplankton, having direct
impact on marine ecosystem dynamic and global environment change, are listed as
essential ocean variables. Phytoplankton classification is very crucial for
Phytoplankton analysis, but it is very difficult because of the huge amount and
tiny volume of Phytoplankton. Machine learning is the principle way of
performing phytoplankton image classification automatically. When carrying out
large-scale research on the marine phytoplankton, the volume of data increases
overwhelmingly and more powerful computational resources are required for the
success of machine learning algorithms. Recently, quantum machine learning has
emerged as the potential solution for large-scale data processing by harnessing
the exponentially computational power of quantum computer. Here, for the first
time, we demonstrate the feasibility of quantum deep neural networks for
phytoplankton classification. Hybrid quantum-classical convolutional and
residual neural networks are developed based on the classical architectures.
These models make a proper balance between the limited function of the current
quantum devices and the large size of phytoplankton images, which make it
possible to perform phytoplankton classification on the near-term quantum
computers. Better performance is obtained by the quantum-enhanced models
against the classical counterparts. In particular, quantum models converge much
faster than classical ones. The present quantum models are versatile, and can
be applied for various tasks of image classification in the field of marine
science.
