Many eigenvalue problems arising in practice are often of the generalized form $A\x=\lambda B\x$. One particularly important case is symmetric, namely $A, B$ are Hermitian and $B$ is positive definite. The standard algorithm for solving this class of eigenvalue problems is to reduce them to Hermitian eigenvalue problems. For a quantum computer, quantum phase estimation is a useful technique to solve Hermitian eigenvalue problems. In this work, we propose a new quantum algorithm for symmetric generalized eigenvalue problems using ordinary differential equations. The algorithm has lower complexity than the standard one based on quantum phase estimation. Moreover, it works for a wider case than symmetric: $B$ is invertible, $B^{-1}A$ is diagonalizable and all the eigenvalues are real.
翻译:实践中产生的许多基因价值问题往往是通用形式的 $A\x ⁇ lambda B\x$。一个特别重要的案例是对称,即$A、B$是Hermitian和$B$是肯定的。解决这种种类的基因价值问题的标准算法是将其减少到Hermitian的基因价值问题。对于量子计算机来说,量子阶段估计是解决Hermitian的基因价值问题的有用技术。在这项工作中,我们提出了使用普通差分方程式处理对称性通用基因价值问题的新量子算法。算法的复杂程度低于以量子阶段估计为基础的标准。此外,算法的复杂程度要小于以量子阶段估计为基础的标准。此外,它适用于比对称范围更广的情况:B美元是不可忽略的,$B ⁇ -1}A$是可分化的,所有基因价值都是真实的。