Published March 19, 2025 | Published
Journal Article Open

Shadow hamiltonian simulation

  • 1. ROR icon Google (United States)
  • 2. ROR icon California Institute of Technology

Abstract

Simulating quantum dynamics is one of the most important applications of quantum computers. Traditional approaches for quantum simulation involve preparing the full evolved state of the system and then measuring some physical quantity. Here, we present a different and novel approach to quantum simulation that uses a compressed quantum state that we call the "shadow state". The amplitudes of this shadow state are proportional to the time-dependent expectations of a specific set of operators of interest, and it evolves according to its own Schrödinger equation. This evolution can be simulated on a quantum computer efficiently under broad conditions. Applications of this approach to quantum simulation problems include simulating the dynamics of exponentially large systems of free fermions or free bosons, the latter example recovering a recent algorithm for simulating exponentially many classical harmonic oscillators. These simulations are hard for classical methods and also for traditional quantum approaches, as preparing the full states would require exponential resources. Shadow Hamiltonian simulation can also be extended to simulate expectations of more complex operators such as two-time correlators or Green's functions, and to study the evolution of operators themselves in the Heisenberg picture.

Copyright and License

© The Author(s) 2025.

This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Acknowledgement

We thank Cristian Batista, Kipton Barros, David Gosset, Robert Huang, William Huggins, Stephen Jordan, Marika Kieferova, Nicholas Rubin, and Nathan Wiebe for discussions.

Contributions

R.D.S., R. King, R. Kothari, T.E.O., R.B. developed the theoretical formalism, contributed to the analysis of results, and to the writing of the manuscript.

Data Availability

No new data was generated or analyzed in this work; data availability is not applicable.

Supplemental Material

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Additional details

Created:
April 3, 2025
Modified:
April 3, 2025