Quantum simulation of nanographenes and Trotter error cancellation

TL;DR

This paper explores quantum simulation of nanographenes, analyzing Trotter errors and revealing error cancellation phenomena that significantly reduce quantum resource requirements for calculating energy gaps.

Contribution

It introduces a tensor-network-based method for spectral analysis and demonstrates Trotter error cancellation in quantum simulations of nanographenes, reducing circuit depth needed.

Findings

Trotter error cancellation reduces circuit depth for energy gap calculations.

Energy gap calculations for large nanographenes are feasible with less than 3.2×10^7 Toffoli gates.

Tensor-network approach enables spectral analysis beyond brute-force methods.

Abstract

Fault-tolerant quantum computing is a promising tool for simulating molecules and materials, but frequently-considered applications require substantial resources, and the gap between hardware capabilities and requirements remains significant. We propose quantum simulation of nanographene $\pi$-systems as relevant and scalable problems to span the gap between early and large-scale fault-tolerant quantum computing. We examine the efficiency of Trotterized quantum simulation, present a detailed analysis of worst-case, average-case and energy eigenvalue Trotter errors, and show that these Trotter error estimates vary by orders of magnitude. Trotter eigenvalue errors are obtained from a novel tensor-network-based approach which allows spectral analysis of product formulas for systems beyond brute-force calculation. Notably, we observe a Trotter error cancellation phenomenon whereby the Trotter error for energy differences between low-lying eigenstates is significantly smaller than the Trotter error for absolute energies, resulting in approximately an order of magnitude circuit depth reduction for quantum phase estimation calculation of energy gaps. This is a significant result because for most chemical applications, only energy differences are of practical relevance. We estimate that calculation of energy gaps to chemical accuracy between the ground- and excited-states within the Pariser--Parr--Pople model for large 2D nanographenes (up to 140 spin orbitals) requires circuits with $< 3.2 \times 10^7$ Toffoli gates. This work shows that considering details of chemically-relevant applications and exploiting error cancellation can lead to substantial reductions in resource requirements.