论文标题
迭代量子优化与自适应问题哈密顿
Iterative Quantum Optimization with Adaptive Problem Hamiltonian
论文作者
论文摘要
量子优化算法有望在实践中解决经典的硬，离散优化问题。但是，用有限的量子数（目前很少）在哈密顿量中编码此类问题的要求但是，仅在该哈密顿尔顿人支持的受限空间内找到最佳的风险。我们描述了一种迭代算法，在该算法中，使用这种限制性问题的解决方案被用来定义一个比前面的问题更适合的新问题。在最短矢量问题的数值示例中，我们表明，具有一系列改进问题的算法汉密尔顿人会收敛到所需的解决方案。
Quantum optimization algorithms hold the promise of solving classically hard, discrete optimization problems in practice. The requirement of encoding such problems in a Hamiltonian realized with a finite -- and currently small -- number of qubits, however, poses the risk of finding only the optimum within the restricted space supported by this Hamiltonian. We describe an iterative algorithm in which a solution obtained with such a restricted problem Hamiltonian is used to define a new problem Hamiltonian that is better suited than the previous one. In numerical examples of the shortest vector problem, we show that the algorithm with a sequence of improved problem Hamiltonians converges to the desired solution.
