Variational Quantum State Diagonalization: Review and Examples

Presenter:
Akash Kundu, Institute of Theoretical and Applied Informatics
Polish Academy of Sciences, Gliwice, Poland

Abstract:
Classical methods of state diagonalization typically scales polynomially in the matrix dimension. On the other hand quantum principle component analysis (qPCA) requires significant number of qubits and gates in its subroutines. And to tackle these issues, quantum state diagonalization using parameterized quantum circuit has been introduced by R. LaRose et.al. We give a brief review of the algorithm, and discuss its application under a class of scenarios.

References:
[1] R. LaRose et.al, Variational quantum state diagonalization, npj Quantum Information 5, 57 (2019). https://doi.org/10.1038/s41534-019-0167-6
[2] S. Lloyd, M. Mohseni, & P. Rebentrost, Quantum principal component analysis. Nat. Phys. 10, 631–633 (2014). https://doi.org/10.1038/nphys3029
[3] M. Cerezo et.al, Variational Quantum Fidelity Estimation, Quantum 4, 248 (2020). https://doi.org/10.22331/q-2020-03-26-248