Simulating the behavior of quantum circuits on classical computers is one of the widely used approaches for current quantum computing device and quantum algorithm research. State- vector simulators keep all the quantum states in main memory, thus consuming a large amount of memory and resulting in significant memory access and communication overhead far longer than calculation time. Consequently, data communication time often becomes the primary bottleneck in quantum circuit simulation. Many classical simulators, such as QuEST, are designed to execute quantum gates serially without exploiting data locality, and all amplitudes need to be exchanged. Such a gate-unaware full-data communication scheme forces powerful computation such as GPUs to spend substantial time waiting for data transfers. In this paper, we propose a gate-aware on-demand communication quantum simulation framework to optimize communication overhead. We analyze the on-demand communication process and develop a communication cost model to elucidate the relationship between communication costs and circuit partition. Since greedy circuit partition method can only yield locally optimal solutions, we propose a choice-start-node quantum circuit partition method guided by the communication cost model to further reduce communication overhead in quantum circuit simulation. We evaluate on a 4-node cluster with 2 NVIDIA A100 GPUs on each node and our design achieves 59.32× speedup compared to the gate-unaware full-data communication scheme. Furthermore, compared to the greedy circuit partition method, our partition method can achieve simulation acceleration of 1.79×.

This paper explores the emotional responses of vibrotactile stimuli in mobile devices. Previous studies have limitations in parameter setting, experimental equipment, and emotion estimation. To address these, we constructed a parameter system and conducted an experiment with 25 participants to explore emotional responses to 300 vibrotactile stimuli. Quantitative results revealed that all 5 parameters significantly influenced emotional responses. Qualitative results indicated that participants’ perceptions of vibrotactile stimuli influenced by various factors, including physical perception differences, personal cognition and experiences, emotional resonance, and crossmodal perception. In addition, an artificial neural network model was developed to estimate vibrotactile emotional responses, demonstrating sufficient accuracy. This study provides guidelines for the emotional design of vibrotactile feedback in mobile devices.

Simulating the behavior of quantum circuits on classical computers are one of the widely used approaches for current quantum computing device and quantum algorithm research. State-vector simulators keep all the quantum states in main memory, thus consuming a large amount of memory and resulting in great memory access and communication overhead far longer than calculation time. Many classical simulators, such as QuEST, are designed by serial execution of quantum gates without using the data locality, and all amplitudes need to be exchanged. Such a gate-unaware full data communication scheme forces powerful computation such as GPUs to spend a lot of time on waiting for data transmission. In this paper, we propose a gate-aware on-demand communication quantum simulation framework to optimize communication overhead. A quantum circuit partition method through gate fusion is first proposed to avoid unnecessary communications. Moreover, a on-demand data communication scheme is proposed to optimize data transfer among computation nodes. Based on these designs, a prototype has been implemented on QuEST. We evaluated on an 8-node cluster with four NVIDIA Tesla V100 GPUs on each node and our designs can achieve 3.0x-15.6x speedup compared to QuEST for 32-34-qubit quantum systems. Moverover, it can scale to simulate 37-qubit quantum system instead of 34-qubit quantum system for QuEST.