Fermionic Partial Tomography via Classical Shadows

نویسندگان

چکیده

We propose a tomographic protocol for estimating any $k$-body reduced density matrix ($k$-RDM) of an $n$-mode fermionic state, ubiquitous step in near-term quantum algorithms simulating many-body physics, chemistry, and materials. Our approach extends the framework classical shadows, randomized to learning collection quantum-state properties, setting. sampling uses measurement settings generated by discrete group Gaussian unitaries, implementable with linear-depth circuits. prove that all $k$-RDM elements additive precision $?$ requires on order $(\genfrac{}{}{0ex}{}{n}{k}){k}^{3/2}\mathrm{log}(n)/{?}^{2}$ repeated state preparations, which is optimal up logarithmic factor. Furthermore, numerical calculations show our offers substantial improvement constant overheads $k\ensuremath{\ge}2$, as compared prior deterministic strategies. also adapt method particle-number symmetry, wherein additional circuit depth may be halved at cost roughly 2--5 times more repetitions.

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ژورنال

عنوان ژورنال: Physical Review Letters

سال: 2021

ISSN: ['1079-7114', '0031-9007', '1092-0145']

DOI: https://doi.org/10.1103/physrevlett.127.110504