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This paper introduces a hybrid simulation technique that integrates Trotter-based quantum evolution with classical dynamic mode decomposition to predict observables in many-body systems without fully reconstructing the wave function. By leveraging short-time quantum measurements, the method achieves a global error bound scaling as $\mathcal{O}(t^{3/2})$, demonstrating practical utility for modeling quench dynamics in Hubbard and spin models. This approach represents a significant step toward efficient quantum simulation where classical post-processing enhances the extraction of physical insights from limited quantum data.
Sources coveringarXiv, Quantum Frontiers, Quantum Zeitgeist
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A hybrid method for quantum dynamics simulation

arXiv2 min read

arXiv:2307.15231v2 Announce Type: replace Abstract: We propose a hybrid approach to simulate quantum many-body dynamics by combining Trotter-based quantum evolution with classical dynamic mode decomposition. In quantum simulation, the interest often lies in estimating observables rather than explicitly obtaining the wave function. Instead of characterizing a quantum state, our method predicts observables of the quantum state at later times by using data from a set of short-time measurements from a quantum computer. For a fixed set of measurements, the global error bound for our method scales at most as $\mathcal{O}(t^{3/2})$, with constants determined by the DMD approximation error and the conditioning of the learned modes. We apply our method to quench dynamics in the Hubbard model and nearest neighbor spin systems and show that the observable properties can be predicted up to a reasonable error by controlling the number of data points obtained from the quantum measurements.

Abstract:We propose a hybrid approach to simulate quantum many-body dynamics by combining Trotter-based quantum evolution with classical dynamic mode decomposition. In quantum simulation, the interest often lies in estimating observables rather than explicitly obtaining the wave function. Instead of characterizing a quantum state, our method predicts observables of the quantum state at later times by using data from a set of short-time measurements from a quantum computer. For a fixed set of measurements, the global error bound for our method scales at most as $\mathcal{O}(t^{3/2})$, with constants determined by the DMD approximation error and the conditioning of the learned modes. We apply our method to quench dynamics in the Hubbard model and nearest neighbor spin systems and show that the observable properties can be predicted up to a reasonable error by controlling the number of data points obtained from the quantum measurements.

arXiv:2307.15231 [quant-ph]

(or arXiv:2307.15231v2 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2307.15231 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1103/33rs-gfqh Focus to learn more DOI(s) linking to related resources

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