Quantum computers can achieve extremely fast computations for certain problems using quantum properties. Due to these factors, research and development in the field of quantum computers have been vigorously pursued. It is crucial to develop a quantum computer that operates accurately and, concurrently, create applications compatible with quantum computing. Quantum computer simulators are tools that represent the behavior of a quantum computer on classical computers, and they are highly conducive to the development of quantum applications that can run on actual quantum computers. On the other hand, quantum computer simulators run on classical computers, which are extremely slow compared to quantum computers. Therefore, it is desirable for the development of quantum applications to be able to fully utilize the computational resources on a classical computer and run at high speed. In this paper, we analyze the performance of the Qiskit Aer simulator, the Qulacs simulator, and mpiQulacs on various types of single servers. We then compare the performance of these quantum computer simulators across different computer environments.

As the computational cost and datasets available for deep neural network training continue to increase, there is a significant demand for fast distributed training on supercomputers. However, porting and tuning applications for new advanced supercomputers requires tremendous amount of development efforts. Therefore, we present software tuning best practice for a 3D-CNN model training on a new Arm CPU based supercomputer, Fugaku. We (i) tune computation in DL by a JIT translator for aarch64, (ii) optimize collective communication such as Allreduce for 6D mesh/torus network topology, (iii) tune I/O by data staging with compression and data loader with caching, and (iv) parallelize training in data and model parallelism. We apply the proposed methods to a CosmoFlow 3D-CNN model, and achieve the training in 30 minutes using 16,384 nodes consisting of 4096 data- and 4 model-parallelism. This is the fastest result of any CPU-based systems in MLPerf HPC v0.7 in the world.

Distributed deep learning using large mini-batches is a key strategy to perform the deep learning as fast as possible, but it represents a great challenge as it is difficult to achieve high scaling efficiency when using large clusters without compromising accuracy. The particular problem in this challenge is decreasing the number of model update iterations in whole of training. Thus, we need a technique which can converge the validation accuracy with a small number of iterations to address this challenge. In this paper, we introduce a novel technique, Final Polishing. This technique adjusts the means and variances in the batch normalization and mitigates the difference of normalization between validation datasets and augmented training datasets. By applying the technique, we achieved top-1 validation accuracy of 75.08% with mini-batch size of 81,920, with 2,048 GPUs and completed the training of ResNet-50 in 74.7 seconds.In addition, targeting top-1 validation accuracy of 75.9% or more, we tried additional parameters tuning. Then, we adjusted the number of GPUs and hyperparameters of DNNs with Final Polishing, and we also achieved top-1 validation accuracy of 75.97% with mini-batch size of 86,016, with 3,072 GPUs and completed the training of ResNet-50 in 62.1 seconds.