A stochastic quantum Krylov protocol with double factorized Hamiltonians

TL;DR

This paper introduces a stochastic quantum Krylov diagonalization method that reduces quantum circuit depth for eigenstate estimation, especially applicable to double-factorized Hamiltonians, demonstrated on molecular systems with high accuracy.

Contribution

The paper presents a novel randomized quantum Krylov algorithm that employs linear combinations of unitaries and stochastic sampling to lower circuit depth for eigenstate problems.

Findings

Achieved ground state energy errors < 1 kcal/mol in simulations.

Circuit depths are significantly shallower than traditional Trotter-Suzuki methods.

Applicable to Hamiltonians with fast-forwardable components.

Abstract

We propose a class of randomized quantum Krylov diagonalization (rQKD) algorithms capable of solving the eigenstate estimation problem with modest quantum resource requirements. Compared to previous real-time evolution quantum Krylov subspace methods, our approach expresses the time evolution operator, $e^{-i\hat{H} \tau}$, as a linear combination of unitaries and subsequently uses a stochastic sampling procedure to reduce circuit depth requirements. While our methodology applies to any Hamiltonian with fast-forwardable subcomponents, we focus on its application to the explicitly double-factorized electronic-structure Hamiltonian. To demonstrate the potential of the proposed rQKD algorithm, we provide numerical benchmarks for a variety of molecular systems with circuit-based statevector simulators, achieving ground state energy errors of less than 1~kcal~mol$^{-1}$ with circuit depths orders of magnitude shallower than those required for low-rank deterministic Trotter-Suzuki decompositions.