  Parallel tensor network contraction algorithms have emerged as the pivotal
benchmarks for assessing the classical limits of computation, exemplified by
Google's demonstration of quantum supremacy through random circuit sampling.
However, the massive parallelization of the algorithm makes it vulnerable to
computer node failures. In this work, we apply coded computing to a practical
parallel tensor network contraction algorithm. To the best of our knowledge,
this is the first attempt to code tensor network contractions. Inspired by
matrix multiplication codes, we provide two coding schemes: 2-node code for
practicality in quantum simulation and hyperedge code for generality. Our
2-node code successfully achieves significant gain for $f$-resilient number
compared to naive replication, proportional to both the number of node failures
and the dimension product of sliced indices. Our hyperedge code can cover
tensor networks out of the scope of quantum, with degraded gain in the exchange
of its generality.
