  Deep learning hardware achieves high throughput and low power consumption by
reducing computing precision and specializing in matrix multiplication. For
machine learning inference, fixed-point value computation is commonplace, where
the input and output values and the model parameters are quantized. Thus, many
processors are now equipped with fast integer matrix multiplication units
(IMMU). It is of significant interest to find a way to harness these IMMUs to
improve the performance of HPC applications while maintaining accuracy. We
focus on the Ozaki scheme, which computes a high-precision matrix
multiplication by using lower-precision computing units, and show the
advantages and disadvantages of using IMMU. The experiment using integer Tensor
Cores shows that we can compute double-precision matrix multiplication faster
than cuBLAS and an existing Ozaki scheme implementation on FP16 Tensor Cores on
NVIDIA consumer GPUs. Furthermore, we demonstrate accelerating a quantum
circuit simulation by up to 4.33 while maintaining the FP64 accuracy.
