# On the Hardness of PAC-learning stabilizer States with Noise

@article{Gollakota2021OnTH, title={On the Hardness of PAC-learning stabilizer States with Noise}, author={Aravind Gollakota and Daniel Liang}, journal={ArXiv}, year={2021}, volume={abs/2102.05174} }

We consider the problem of learning stabilizer states with noise in the Probably Approximately Correct (PAC) framework of Aaronson [Aar07] for learning quantum states. In the noiseless setting, an algorithm for this problem was recently given by Rocchetto [Roc18], but the noisy case was left open. Motivated by approaches to noise tolerance from classical learning theory, we introduce the Statistical Query (SQ) model for PAC-learning quantum states, and prove that algorithms in this model are… Expand

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