Recent technological advancements show promise in leveraging quantum mechanical phenomena for computation. This brings substantial speed-ups to problems that are once considered to be intractable in the classical world. However, the physical realization of quantum computers is still far away from us, and a majority of research work is done using quantum simulators running on classical computers. Classical computers face a critical obstacle in simulating quantum algorithms. Quantum states reside in a Hilbert space whose size grows exponentially to the number of subsystems, i.e., qubits. As a result, the straightforward statevector approach does not scale due to the exponential growth of the memory requirement. Decision diagrams have gained attention in recent years for representing quantum states and operations in quantum simulations. The main advantage of this approach is its ability to exploit redundancy. However, mainstream quantum simulators still rely on statevectors or tensor networks. We consider the absence of decision diagrams due to the lack of parallelization strategies. This work explores several strategies for parallelizing decision diagram operations, specifically for quantum simulations. We propose optimal parallelization strategies. Based on the experiment results, our parallelization strategy achieves a 2-3 times faster simulation of Grover's algorithm and random circuits than the state-of-the-art single-thread DD-based simulator DDSIM.

翻译：近期技术进步在利用量子力学现象进行计算方面展现出希望，为那些曾被认为在经典世界中难以处理的问题带来了显著的加速。然而，量子计算机的物理实现仍遥不可及，大多数研究工作依赖于在经典计算机上运行的量子模拟器。经典计算机在模拟量子算法时面临关键障碍：量子态存在于一个希尔伯特空间中，其大小随子系统（即量子比特）数量呈指数级增长。因此，直接的状态向量方法因内存需求呈指数级增长而无法扩展。近年来，决策图在量子模拟中用于表示量子态和操作引起了关注，该方法的主要优势在于能够利用冗余性。然而，主流量子模拟器仍依赖状态向量或张量网络。我们认为决策图的缺失是由于缺乏并行化策略。本文探讨了针对量子模拟中决策图操作的几种并行化策略，并提出了最优的并行化方案。基于实验结果，我们的并行化策略在Grover算法和随机电路的模拟中，比现有最先进的单线程决策图模拟器DDSIM快2到3倍。