Quantum emulators play an important role in the development and testing of quantum algorithms, especially given the limitations of the current FTQC era. Developing high-speed, memory-optimized quantum emulators is a growing research trend, with gate fusion being a promising technique. However, existing gate fusion implementations often struggle to efficiently support large-scale quantum systems with a high number of qubits due to a lack of optimizations for the exponential growth in memory requirements. Therefore, this study proposes the EMMS (Efficient-Memory Matrix Storage) method for storing quantum operators and states, along with an EMMS-based Quantum Emulator Accelerator (QEA) architecture that incorporates multiple processing elements (PEs) to accelerate tensor product and matrix multiplication computations in quantum emulation with gate fusion. The theoretical analysis of the QEA on the Xilinx ZCU102 FPGA, using varying numbers of PEs and different depths of unitary and local data memory, reveals a linear increase in memory depth with the number of qubits. This scaling highlights the potential of the EMMS-based QEA to accommodate larger quantum circuits, providing insights into selecting appropriate memory sizes and FPGA devices. Furthermore, the estimated performance of the QEA with PE counts ranging from $2^2$ to $2^5$ on the Xilinx ZCU102 FPGA demonstrates that increasing the number of PEs significantly reduces the computation cycle count for circuits with fewer than 18 qubits, making it significantly faster than previous works.

翻译：量子模拟器在量子算法的开发和测试中扮演着重要角色，尤其是在当前容错量子计算时代存在局限性的背景下。开发高速、内存优化的量子模拟器是一个日益增长的研究趋势，其中门融合是一项前景广阔的技术。然而，现有的门融合实现往往难以高效支持具有大量量子比特的大规模量子系统，原因在于缺乏对内存需求指数级增长的优化。因此，本研究提出了用于存储量子算符和状态的EMMS（高效内存矩阵存储）方法，以及一种基于EMMS的量子模拟加速器架构。该架构集成了多个处理单元，以加速采用门融合的量子模拟中的张量积和矩阵乘法计算。在Xilinx ZCU102 FPGA上对QEA进行的理论分析，通过使用不同数量的PE以及不同深度的酉矩阵和本地数据内存，揭示了内存深度随量子比特数量线性增长的特性。这种缩放特性凸显了基于EMMS的QEA适应更大规模量子电路的潜力，并为选择合适的内存大小和FPGA设备提供了见解。此外，在Xilinx ZCU102 FPGA上对PE数量从$2^2$到$2^5$的QEA进行的性能估算表明，增加PE数量能显著减少少于18个量子比特的电路的计算周期数，使其计算速度显著快于先前的研究工作。