Presentation: The power of block-encoded matrix powers: improved regression techniques via faster Hamiltonian simulation
We apply the framework of block-encodings, introduced by Low and Chuang (under the name standard-form), to the study of quantum machine learning algorithms using quantum accessible data structures. We develop several tools within the block-encoding framework, including quantum linear system solvers using block-encodings. Our results give new techniques for Hamiltonian simulation of non-sparse matrices, which could be relevant for certain quantum chemistry applications, and which in turn imply an exponential improvement in the dependence on precision in quantum linear systems solvers for non-sparse matrices.
|Project||WISE Women In Science Excel , Progress in quantum computing:Algorithms, communication, and applications|
|Conference||Challenges in Quantum Computation|
Jeffery, S. (2018). Presentation: The power of block-encoded matrix powers: improved regression techniques via faster Hamiltonian simulation.
|Challenges in Quantum Computation Presentation|