Ground and excited-state energies with analytic errors and short time evolution on a quantum computer

TL;DR

This paper introduces a novel quantum algorithm that estimates ground and excited state energies accurately by analyzing autocorrelation functions, avoiding direct wave function calculations, and leveraging spectral methods with error bounds.

Contribution

It proposes quantum prolate diagonalization (QPD), a hybrid classical-quantum approach that achieves chemical accuracy in energy estimation at the Heisenberg limit using a new spectral approximation framework.

Findings

QPD estimates energies within chemical accuracy.

Error bounds reveal a sharp accuracy transition based on observation time.

High precision maintained even with imperfect state preparation.

Abstract

Accurately solving the Schr\"odinger equation remains a central challenge in computational physics, chemistry, and materials science. Here, we propose an alternative eigenvalue problem based on a system's autocorrelation function, avoiding direct reference to a wave function. In particular, we develop a rigorous approximation framework that enables precise frequency estimation from a finite number of signal samples. Our analysis builds on new results involving prolate spheroidal wave functions and yields error bounds that reveal a sharp accuracy transition governed by the observation time and spectral density of the signal. These results are very general and thus carry far. As one important example application we consider the quantum computation for molecular systems. By combining our spectral method with a quantum subroutine for signal generation, we define quantum prolate diagonalization (QPD) - a hybrid classical-quantum algorithm. QPD simultaneously estimates ground and excited state energies within chemical accuracy at the Heisenberg limit. An analysis of different input states demonstrates the robustness of the method, showing that high precision can be retained even under imperfect state preparation.