Model Independent Error Mitigation in Parametric Quantum Circuits and Depolarizibility of Quantum Noise

28 Jul 2021, 21:15
15m
Oral presentation Algorithms (including Machine Learning, Quantum Computing, Tensor Networks) Algorithms (including Machine Learning, Quantum Computing, Tensor Networks)

Speaker

Xiaoyang Wang (Peking University)

Description

Finding ground states and low-lying excitations of Hamiltonians is one of the most important problems that can be solved with near-term quantum computers. It can be utilized in fields ranging from optimization over chemistry and material science to particle physics. In this work, we propose an efficient error mitigation scheme that is independent of the Hamiltonian and the concrete noise model, applicable to low-depth quantum circuits as they occur in Variational Quantum Eigensolvers (VQE). In principle, our method can eliminate all systematic errors by exploring the depolarizibility of quantum noise up to certain approximations. We carry out both classical simulations and experiments on the IBM's quantum hardware, to illustrate the performance of the method by computing the mass gap of transversal Ising model and extracting its the zero-temperature critical point.

Author

Xiaoyang Wang (Peking University)

Co-authors

Prof. Xu Feng (Peking University) Prof. Karl Jansen (DESY)

Presentation materials