  Advances in quantum simulator technology is increasingly required because
research on quantum algorithms is becoming more sophisticated and complex.
State vector simulation utilizes CPU and memory resources in computing nodes
exponentially with respect to the number of qubits; furthermore, in a
variational quantum algorithm, the large number of repeated runs by classical
optimization is also a heavy load. This problem has been addressed by preparing
numerous computing nodes or simulation frameworks that work effectively. This
study aimed to accelerate quantum simulation using two newly proposed methods:
to efficiently utilize limited computational resources by adjusting the ratio
of the MPI and distributed processing parallelism corresponding to the target
problem settings and to slim down the Hamiltonian by considering the effect of
accuracy on the calculation result. Ground-state energy calculations of
fermionic model were performed using variational quantum eigensolver (VQE) on
an HPC cluster with up to 1024 FUJITSU Processor A64FX connected to each other
by InfiniBand; the processor is also used on supercomputer Fugaku. We achieved
200 times higher speed over VQE simulations and demonstrated 32 qubits
ground-state energy calculations in acceptable time. This result indicates that
> 30 qubit state vector simulations can be realistically utilized to further
research on variational quantum algorithms.
