Stochastic Approach For Simulating Quantum Noise Using Tensor Networks

• URL: http://arxiv.org/abs/2210.15874v1
• Date: Fri, 28 Oct 2022 03:44:59 GMT
• Title: Stochastic Approach For Simulating Quantum Noise Using Tensor Networks
• Authors: William Berquist, Danylo Lykov, Minzhao Liu, Yuri Alexeev
• Abstract summary: We show that our simulation error is relatively low, even for large numbers of qubits. By using the slicing technique, we can simulate up to 100 qubitOA circuits with high depth using supercomputers.
• Score: 0.8258451067861933
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 $n\lesssim 30$, 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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