  The large variation of datasets is a huge barrier for image classification
tasks. In this paper, we embraced this observation and introduce the finite
temperature tensor network (FTTN), which imports the thermal perturbation into
the matrix product states framework by placing all images in an environment
with constant temperature, in analog to energy-based learning. Tensor network
is chosen since it is the best platform to introduce thermal fluctuation.
Different from traditional network structure which directly takes the summation
of individual losses as its loss function, FTTN regards it as thermal average
loss computed from the entanglement with the environment. The temperature-like
parameter can be automatically optimized, which gives each database an
individual temperature. FTTN obtains improvement in both test accuracy and
convergence speed in several datasets. The non-zero temperature automatically
separates similar features, avoiding the wrong classification in previous
architecture. The thermal fluctuation may give a better improvement in other
frameworks, and we may also implement the temperature of database to improve
the training effect.
