Molecular Properties from Quantum Krylov Subspace Diagonalization

TL;DR

This paper advances quantum Krylov subspace methods by deriving derivatives, reducing measurement complexity, and efficiently computing molecular properties, enabling more practical quantum simulations of molecules.

Contribution

It introduces analytical derivatives, reduces measurement scaling using quantum signal processing, and demonstrates improved methods for molecular property calculations.

Findings

Reduced measurement scaling to a constant using quantum signal processing.

Validated approach by computing nuclear gradients of a small molecule.

Compared measurement schemes for expectation value estimation.

Abstract

Quantum Krylov subspace diagonalization is a prominent candidate for early fault tolerant quantum simulation of many-body and molecular systems, but so far the focus has been mainly on computing ground-state energies. We go beyond this by deriving analytical first-order derivatives for quantum Krylov methods and show how to obtain relaxed one and two particle reduced density matrices of the Krylov eigenstates. The direct approach to measuring these matrices requires a number of distinct measurement that scales quadratically with the Krylov dimension $D$. Here, we show how to reduce this scaling to a constant. This is done by leveraging quantum signal processing to prepare Krylov eigenstates, including exited states, in depth linear in $D$. We also compare several measurement schemes for efficiently obtaining the expectation value of an operator with states prepared using quantum signal processing. We validate our approach by computing the nuclear gradient of a small molecule and estimating its variance.