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This paper proposes a method for checking whether quantum simulations of magnetic spin dynamics agree with inelastic neutron scattering experiments and classical many-body calculations. Its main contribution is a shared framework for comparing outputs that come from different native observables, with explicit uncertainty and distortion tracking. For quantum computing readers, the practical point is that hardware benchmarking against experiments needs this kind of rigor before results can be trusted in regimes that classical methods can't check.
A Validation Framework for Quantum Simulation of Spin Dynamics against Inelastic Neutron Scattering and Classical Simulation
arXiv:2607.01568v2 Announce Type: replace Abstract: Establishing quantum simulations as reliable tools for materials research requires quantitative validation against experiments and, where available, classical many-body calculations. For dynamical spin response, such validation remains challenging because experiment, classical simulation, and quantum simulation do not produce the same native observables. This challenge is increasingly relevant as quantum simulation protocols for dynamical response progress from theory to hardware-level benchmarking against neutron scattering data. The longer-term goal is to extend validation to classically intractable regimes accessible through future fault-tolerant implementations. Here, we develop a cross-pipeline validation framework for quantum simulation of dynamical spin response, using inelastic neutron scattering and classical many-body simulation as complementary experimental and computational anchors. The framework brings these pipelines to common momentum- and energy-resolved comparison levels through explicit forward and inverse observable maps, propagates uncertainty via covariance matrices or resampling, tests robustness to structured distortion, and evaluates agreement using a hierarchy of complementary metric families. It distinguishes stochastic uncertainty from robustness-induced distortion, carries both through the comparison chain, and uses the resulting metric-level uncertainty and distortion information to support layered validation at the pipeline, solver, and model levels. We also introduce actuator-aware feedback logic to improve agreement without obscuring the physical origin of any remaining mismatch. We close by outlining future extensions of this methodology, including upstream uncertainty and distortion modeling, adaptive feedback, asymmetric validation beyond full classical benchmarking, fault-tolerant workflows, and community infrastructure for reproducible validation.
Authors:Gilles Buchs, Elaine Wong, Anshumitra Baul, Kathleen E. Hamilton, Arnab Banerjee, Stephan Eidenbenz, Gábor B. Halász, Paul Kairys, Keerthi Kumaran, Thomas Maier, Thomas Naughton III, Elijah Pelofske, Vincent Russo, Allen Scheie, Yigit Subasi, D. Alan Tennant, Akram Touil, Travis S. Humble, Andrew T. Sornborger
Abstract:Establishing quantum simulations as reliable tools for materials research requires quantitative validation against experiments and, where available, classical many-body calculations. For dynamical spin response, such validation remains challenging because experiment, classical simulation, and quantum simulation do not produce the same native observables. This challenge is increasingly relevant as quantum simulation protocols for dynamical response progress from theory to hardware-level benchmarking against neutron scattering data. The longer-term goal is to extend validation to classically intractable regimes accessible through future fault-tolerant implementations. Here, we develop a cross-pipeline validation framework for quantum simulation of dynamical spin response, using inelastic neutron scattering and classical many-body simulation as complementary experimental and computational anchors. The framework brings these pipelines to common momentum- and energy-resolved comparison levels through explicit forward and inverse observable maps, propagates uncertainty via covariance matrices or resampling, tests robustness to structured distortion, and evaluates agreement using a hierarchy of complementary metric families. It distinguishes stochastic uncertainty from robustness-induced distortion, carries both through the comparison chain, and uses the resulting metric-level uncertainty and distortion information to support layered validation at the pipeline, solver, and model levels. We also introduce actuator-aware feedback logic to improve agreement without obscuring the physical origin of any remaining mismatch. We close by outlining future extensions of this methodology, including upstream uncertainty and distortion modeling, adaptive feedback, asymmetric validation beyond full classical benchmarking, fault-tolerant workflows, and community infrastructure for reproducible validation.
arXiv:2607.01568 [quant-ph]
(or arXiv:2607.01568v2 [quant-ph] for this version)
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