AI 中文总结
介绍一种受模型预测控制启发的混合量子经典算法，统一VQA与FQA设计，用逐层滚动时域策略优化变分电路参数，可用于基态制备等，数学证明和模拟表明其性能至少与FQA相当，实际应用中表现更好。
AI 中文摘要
我们引入了一种受称为模型预测控制（MPC）的先进控制策略启发的新型混合量子经典算法。该算法将变分量子算法（VQA）基于优化的设计与反馈量子算法（FQA）基于反馈的设计统一起来。变分电路参数使用逐层滚动时域策略进行优化，每层后的可观测量测量初始化一个经典模拟的动态模型，用于预测量子态演化并优化未来的参数化门。这种混合算法可用于基态制备和近似组合优化等应用。通过数学证明和数值证据表明，基于MPC的算法至少能与FQA的性能相匹配。在最大割问题和二维横向场伊辛模型上的模拟表明，基于MPC算法的宽松实现相比FQA在实际中也能提供更好的性能。
英文摘要
We introduce a new hybrid quantum-classical algorithm inspired by an advanced control strategy known as model predictive control (MPC). This algorithm unifies the optimization-based design of variational quantum algorithms (VQAs) with the feedback-based design of feedback-based quantum algorithms (FQAs). Variational circuit parameters are optimized using a layer-wise receding horizon strategy, where observable measurements after every layer initialize a classically simulated dynamic model used to predict quantum state evolution and optimize over future parameterized gates. This hybrid algorithm can be used for applications such as ground state preparation and approximate combinatorial optimization, and presents an ideal use case for the integration of quantum computers with high-performance computing, where the latter resource can be used to increase the scale and efficiency of the predictions critical to MPC. We show through mathematical proof and numerical evidence that the MPC-based algorithm can be guaranteed to at least match the performance of FQAs. Through simulations on Max-Cut problems and a two-dimensional transverse-field Ising model, we demonstrate that relaxed implementations of the MPC-based algorithm can also provide improved performance in practice compared to an FQA.
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