Benchmarking quantum simulation with neutron-scattering experiments
Yi-Ting Lee, Keerthi Kumaran, Bibek Pokharel, Allen Scheie, Colin L. Sarkis, David A. Tennant, Travis Humble, Andr\'e Schleife, Abhinav Kandala, and Arnab Banerjee

TL;DR
This paper demonstrates that a 50-qubit superconducting quantum processor can perform meaningful quantum simulations of material spectra, matching neutron-scattering experiments and extending to complex models with strong correlations.
Contribution
It introduces a quantum-classical workflow for computing dynamical structure factors, benchmarking quantum simulations against experimental data for the first time at this scale.
Findings
Quantum processor achieves quantitative agreement with neutron-scattering data for KCuF3.
Simulation accuracy depends on circuit depth and fidelity.
Extended simulations to a gapped XXZ model with NNN interactions.
Abstract
Realistic simulation of quantum materials is a central goal of quantum computation. Although quantum processors have advanced rapidly in scale and fidelity, it has remained unclear whether pre-fault-tolerant devices can perform quantitatively reliable material simulations. We demonstrate that a superconducting quantum processor operating on up to 50 qubits can already produce meaningful, quantitative comparisons with inelastic neutron-scattering measurements of KCuF, a canonical realization of a gapless Luttinger liquid system with a strongly correlated ground state and a spectrum of emergent spinons. The quantum simulation is enabled by a quantum-classical workflow for computing dynamical structure factors (DSFs). The resulting spectra are benchmarked against experimental measurements using multiple metrics, highlighting the impact of circuit depth and circuit fidelity on…
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