Error mitigation in variational quantum eigensolvers using probabilistic machine learning
J. Rogers, Gargee Bhattacharyya, Marius S. Frank, Tao Jiang, Ove Christiansen, Yongxin Yao, Nicola Lanatà · arXiv (Cornell University) · 2021
Quantum-classical hybrid schemes based on variational quantum eigensolvers (VQEs) may transform our ability of simulating materials and molecules already within the next few years. However, one of the main obstacles to overcome in order to achieve practical near-term quantum advantage is to improve our ability of mitigating the effects, characteristic of the current generation of quantum processing units (QPUs). To this end, here we design a method based on probabilistic machine learning, which allows us to mitigate the noise by imbuing within the computation prior (data independent) information about the variational landscape. We perform benchmark calculations of a 4-qubit impurity model using the IBM open-source framework for quantum computing Qiskit, showing that our method improves dramatically the accuracy of the VQE outputs. Finally, we show that applying our method makes quantum-embedding simulations of the Hubbard model with a VQE impurity solver considerably more reliable.