论文标题
具有工程可能性功能的贝叶斯推断的基础，用于稳健振幅估计
Foundations for Bayesian inference with engineered likelihood functions for robust amplitude estimation
论文作者
论文摘要
我们为使用工程似然函数（ELFS）介绍了数学和概念基础，以实现稳健振幅估算的任务，这是Wang等人在Wang等人中引入的框架。 [PRX量子2，010346（2021）]使用贝叶斯推断来提高量子采样中信息增益的速率。这些通过在参数化的量子电路中选择可调参数来获得的这些ELF来最大程度地减少估计参数的预期后差异，它在估计量子观测值的期望值中起着重要作用。我们对某些类别的量子电路产生的似然函数进行了彻底的表征和分析，并将其与贝叶斯推断的工具相结合，以提供选择最佳精灵可调参数的过程。最后，我们提出数值结果以证明精灵的性能。
We present mathematical and conceptual foundations for the task of robust amplitude estimation using engineered likelihood functions (ELFs), a framework introduced in Wang et al. [PRX Quantum 2, 010346 (2021)] that uses Bayesian inference to enhance the rate of information gain in quantum sampling. These ELFs, which are obtained by choosing tunable parameters in a parametrized quantum circuit to minimize the expected posterior variance of an estimated parameter, play an important role in estimating the expectation values of quantum observables. We give a thorough characterization and analysis of likelihood functions arising from certain classes of quantum circuits and combine this with the tools of Bayesian inference to give a procedure for picking optimal ELF tunable parameters. Finally, we present numerical results to demonstrate the performance of ELFs.
