Any linear space of square matrices has an associated eigenvector variety. Its points are eigenvectors of matrices from that linear space. We present a systematic study of eigenvector varieties, with focus on Lie algebras and Hamiltonians of quantum systems.
A common challenge faced in quantum physics is finding the extremal eigenvalues and eigenvectors of a Hamiltonian matrix in a vector space so large that linear algebra operations on general vectors are not possible. There are numerous efficient methods developed for this task, but they generally fail when some control parameter in the Hamiltonian matrix exceeds some threshold value. In this Letter we present a new technique called eigenvector continuation that can extend the reach of these methods. The key insight is that while an eigenvector resides in a linear space with enormous dimensions, the eigenvector trajectory generated by smooth changes of the Hamiltonian matrix is well approximated by a very low-dimensional manifold. We prove this statement using analytic function theory and propose an algorithm to solve for the extremal eigenvectors. We benchmark the method using several examples from quantum many-body theory.
This chapter introduces the essential topics and to show how eigenvalues and eigenvectors are calculated. In quantum computation it is relevant to consider cases in which the eigenvalues are all real and the eigenvectors are orthogonal. In the study of quantum computation, the matrices with which we usually work have properties that enable us to be quite specific about the nature of the eigenvalues and eigenvectors. Post-measurement the system is in a state represented by an eigenvector or eigenvectors. In quantum computation it is relevant to consider cases in which the eigenvalues are all real and the eigenvectors are orthogonal.