Exact and efficient Lanczos method on a quantum computer

TL;DR

This paper introduces an exact, polynomial-time quantum algorithm for constructing Krylov spaces for Hamiltonian eigenvalue estimation, avoiding the need for time evolution simulations and providing explicit error bounds.

Contribution

It presents a novel quantum algorithm that exactly constructs Krylov spaces efficiently, differing from prior methods by not requiring time evolution simulations.

Findings

Achieves polynomial time and memory complexity for Krylov space construction.

Provides explicit error bounds for ground state energy estimates.

Does not require simulating real or imaginary time evolution.

Abstract

We present an algorithm that uses block encoding on a quantum computer to exactly construct a Krylov space, which can be used as the basis for the Lanczos method to estimate extremal eigenvalues of Hamiltonians. While the classical Lanczos method has exponential cost in the system size to represent the Krylov states for quantum systems, our efficient quantum algorithm achieves this in polynomial time and memory. The construction presented is exact in the sense that the resulting Krylov space is identical to that of the Lanczos method, so the only approximation with respect to the exact method is due to finite sample noise. This is possible because, unlike previous quantum Krylov methods, our algorithm does not require simulating real or imaginary time evolution. We provide an explicit error bound for the resulting ground state energy estimate in the presence of noise. For our method to be successful efficiently, the only requirement on the input problem is that the overlap of the initial state with the true ground state must be $\Omega(1/\text{poly}(n))$ for $n$ qubits.