Generalized quantum singular value transformation with application in quantum bi-conjugate gradient method

Quantum signal processing (QSP) and generalized quantum signal processing (GQSP) are essential tools for implementing the block encoding of matrix functions. The achievable polynomials of QSP have restrictions on parity, while GQSP eliminates these restrictions. In this paper, we further investigate GQSP and present a quantum bi-conjugate gradient (BiCG) algorithm as an application. First, we extend GQSP, which constructs functions of unitary matrices, to general matrices. We refer to this extension as generalized quantum singular value transformation (GQSVT). Subsequently, we implement the quantum BiCG method, utilizing GQSVT and swap test, which has a relatively shallow circuit depth and requires a small number of ancilla qubits.