Efficient quantum computing simulation through dynamic matrix restructuring and distributed evaluation

Quantum computing has the potential to revolutionize the field of computing, but with hardware and algorithms unlike any in use today. Due to the primitive state of existing quantum hardware, simulation is one of the most effective methods for studying quantum computing issues. Our team previously developed a general-purpose simulator capable of modeling arbitrary quantum algorithms executing on any hardware device. The simulator performs over a thousand matrix multiplications per step as part of its operations. To improve the simulator performance, optimizations were designed to dynamically restructure the problem. The resulting calculations were then distributed across a cluster. These enhancements reduced both the order of the simulator operations and the memory overhead, achieving an overall performance improvement of 99.94% from the initial implementation of a key quantum algorithm, reducing the simulator run time for this algorithm from two days on a single processor to under two minutes on a cluster.