  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.
