Learning fermionic correlations by evolving with random translationally invariant Hamiltonians

• URL: http://arxiv.org/abs/2309.12933v1
• Date: Fri, 22 Sep 2023 15:31:39 GMT
• Title: Learning fermionic correlations by evolving with random translationally invariant Hamiltonians
• Authors: Janek Denzler, Antonio Anna Mele, Ellen Derbyshire, Tommaso Guaita, and Jens Eisert
• Abstract summary: We provide a measurement scheme for fermionic quantum devices. We precisely characterize what correlation functions can be recovered and equip the estimates with rigorous bounds on sample complexities. On a conceptual level, this work brings the idea of classical shadows to the realm of large scale analog quantum simulators.
• Score: 0.2796197251957244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Schemes of classical shadows have been developed to facilitate the read-out of digital quantum devices, but similar tools for analog quantum simulators are scarce and experimentally impractical. In this work, we provide a measurement scheme for fermionic quantum devices that estimates second and fourth order correlation functions by means of free fermionic, translationally invariant evolutions - or quenches - and measurements in the mode occupation number basis. We precisely characterize what correlation functions can be recovered and equip the estimates with rigorous bounds on sample complexities, a particularly important feature in light of the difficulty of getting good statistics in reasonable experimental platforms, with measurements being slow. Finally, we demonstrate how our procedure can be approximately implemented with just nearest-neighbour, translationally invariant hopping quenches, a very plausible procedure under current experimental requirements, and requiring only random time-evolution with respect to a single native Hamiltonian. On a conceptual level, this work brings the idea of classical shadows to the realm of large scale analog quantum simulators.

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