Risk-sensitive Optimization for Robust Quantum Controls
- URL: http://arxiv.org/abs/2104.01323v1
- Date: Sat, 3 Apr 2021 06:44:10 GMT
- Title: Risk-sensitive Optimization for Robust Quantum Controls
- Authors: Xiaozhen Ge and Re-Bing Wu
- Abstract summary: We show that the robustness of high-precision controls can be remarkably enhanced through sampling-based optimization of a risk-sensitive loss function.
We propose two algorithms, which are tunable as the risk-sensitive GRAPE and the adaptive risk-sensitive GRAPE.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Highly accurate and robust control of quantum operations is vital for the
realization of error-correctible quantum computation. In this paper, we show
that the robustness of high-precision controls can be remarkably enhanced
through sampling-based stochastic optimization of a risk-sensitive loss
function. Following the stochastic gradient-descent direction of this loss
function, the optimization is guided to penalize poor-performance uncertainty
samples in a tunable manner. We propose two algorithms, which are termed as the
risk-sensitive GRAPE and the adaptive risk-sensitive GRAPE. Their effectiveness
is demonstrated by numerical simulations, which is shown to be able to achieve
high control robustness while maintaining high fidelity.
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