Many quantum algorithms have daunting resource requirements when compared to
what is available today. To address this discrepancy, a quantum-classical
hybrid optimization scheme known as "the quantum variational eigensolver" was
developed with the philosophy that even minimal quantum resources could be made
useful when used in conjunction with classical routines. In this work we extend
the general theory of this algorithm and suggest algorithmic improvements for
practical implementations. Specifically, we develop a variational adiabatic
ansatz and explore unitary coupled cluster where we establish a connection from
second order unitary coupled cluster to universal gate sets through relaxation
of exponential splitting. We introduce the concept of quantum variational error
suppression that allows some errors to be suppressed naturally in this
algorithm on a pre-threshold quantum device. Additionally, we analyze
truncation and correlated sampling in Hamiltonian averaging as ways to reduce
the cost of this procedure. Finally, we show how the use of modern derivative
free optimization techniques can offer dramatic computational savings of up to
three orders of magnitude over previously used optimization techniques.

本文讨论了量子算法中的优化问题，并提出了一种基于量子 - 经典混合的优化方案，其中包括变分绝热量子算法和单粒子门晶格，通过拟合和消除误差和采用现代没有导数的优化技术来节省计算成本。