Mitigating off-resonant error in the cross-resonance gate
Mohammad Moein Malekakhlagh, Easwar Magesan
APS March Meeting 2022
Variational quantum algorithms represent a powerful approach for solving optimization problems on noisy quantum computers, with a broad spectrum of potential applications ranging from chemistry to machine learning. However, their performances in practical implementations crucially depend on the effectiveness of quantum circuit training, which can be severely limited by phenomena such as barren plateaus. While, in general, dissipation is detrimental for quantum algorithms, and noise itself can actually induce barren plateaus, here we describe how the inclusion of properly engineered Markovian losses after each unitary quantum circuit layer allows for the trainability of quantum models. We identify the required form of the dissipation processes and establish that their optimization is efficient. We benchmark the generality of our proposal in both a synthetic and a practical quantum chemistry example, demonstrating its effectiveness and potential impact across different domains.
Mohammad Moein Malekakhlagh, Easwar Magesan
APS March Meeting 2022
Weixin Lu, Jaewoo Lee, et al.
APS March Meeting 2023
Thomas Steckmann, Alireza Seif, et al.
APS March Meeting 2024
Elisa Bäumer, Vinay Tripathi, et al.
APS March Meeting 2024