Eliminating Redundant Computation in Noisy Quantum Computing Simulation

Noisy Quantum Computing (QC) simulation on a classical machine is very time consuming since it requires Monte Carlo simulation with a large number of error-injection trials to model the effect of random noises. Orthogonal to existing QC simulation optimizations, we aim to accelerate the simulation by eliminating the redundant computation among those Monte Carlo simulation trials. We observe that the intermediate states of many trials can often be the same. Once these states are computed in one trial, they can be temporarily stored and reused in other trials. However, storing such states will consume significant memory space. To leverage the shared intermediate states without introducing too much storage overhead, we propose to statically generate and analyze the Monte Carlo simulation simulation trials before the actual simulation. Those trials are reordered to maximize the overlapped computation between two consecutive trials. The states that cannot be reused in follow-up simulation are dropped, so that we only need to store a few states. Experiment results show that the proposed optimization scheme can save on average 80% computation with only a small number of state vectors stored. In addition, the proposed simulation scheme demonstrates great scalability as more computation can be saved with more simulation trials or on future QC devices with reduced error rates.