SC22 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

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Stochastic Approach for Simulating Quantum Noise Using Tensor Networks

Workshop: Third International Workshop on Quantum Computing Software

Authors: William Berquist (Argonne National Laboratory (ANL), University of Houston) and Danylo Lykov, Minzhao Liu, and Yuri Alexeev (Argonne National Laboratory (ANL), University of Chicago)

Abstract: Noisy quantum simulation is challenging since one has to take into account the stochastic nature of the process. The dominating method for it is the density matrix approach. In this paper, we evaluate conditions for which this method is inferior to a substantially simpler way of simulation. Our approach uses stochastic ensembles of quantum circuits, where random Kraus operators are applied to original quantum gates to represent random errors for modeling quantum channels. We show that our stochastic simulation error is relatively low, even for large numbers of qubits. We implemented this approach as a part of the QTensor package. While usual density matrix simulations on average hardware are challenging at n>15, we show that for up to n<30, it is possible to run embarrassingly parallel simulations with <1% error. By using the tensor slicing technique, we can simulate up to 100 qubit QAOA circuits with high depth using supercomputers.

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