Exponentially More Precise Quantum Simulation of Fermions in the Configuration Interaction Representation

TL;DR

This paper introduces a quantum algorithm that significantly improves the efficiency of simulating molecular systems by using a configuration interaction matrix representation, reducing resource requirements and achieving exponential precision improvements.

Contribution

The paper presents a novel quantum simulation algorithm utilizing a CI matrix oracle, achieving exponential precision and reduced qubit requirements compared to previous methods.

Findings

Gate count scales as                    

Achieves logarithmic scaling with inverse precision using a truncated Taylor series

Uses a sparse, first-quantized CI matrix oracle for on-the-fly molecular integral computation

Abstract

We present a quantum algorithm for the simulation of molecular systems that is asymptotically more efficient than all previous algorithms in the literature in terms of the main problem parameters. As in previous work [Babbush et al., New Journal of Physics 18, 033032 (2016)], we employ a recently developed technique for simulating Hamiltonian evolution, using a truncated Taylor series to obtain logarithmic scaling with the inverse of the desired precision. The algorithm of this paper involves simulation under an oracle for the sparse, first-quantized representation of the molecular Hamiltonian known as the configuration interaction (CI) matrix. We construct and query the CI matrix oracle to allow for on-the-fly computation of molecular integrals in a way that is exponentially more efficient than classical numerical methods. Whereas second-quantized representations of the wavefunction require $\widetilde{\cal O}(N)$ qubits, where $N$ is the number of single-particle spin-orbitals, the CI matrix representation requires $\widetilde{\cal O}(\eta)$ qubits where $\eta \ll N$ is the number of electrons in the molecule of interest. We show that the gate count of our algorithm scales at most as $\widetilde{\cal O}(\eta^2 N^3 t)$.