Real classical shadows

TL;DR

This paper introduces a real-valued classical shadow protocol using orthogonal Clifford gates, which reduces sample complexity for estimating expectation values of quantum states, especially for real-valued and local observables.

Contribution

It demonstrates that real orthogonal Clifford-based shadows improve sample efficiency over standard Clifford schemes, including exponential reductions for certain local observables.

Findings

Global orthogonal Cliffords halve the samples needed for real-valued observables.

Local orthogonal Cliffords exponentially reduce samples for k-local real-valued Pauli observables.

Orthogonal shadows can replicate the original unitary shadows protocol with complex basis measurements.

Abstract

Efficiently learning expectation values of a quantum state using classical shadow tomography has become a fundamental task in quantum information theory. In a classical shadows protocol, one measures a state in a chosen basis W after it has evolved under a unitary transformation randomly sampled from a chosen distribution U. In this work we study the case where U corresponds to either local or global orthogonal Clifford gates, and W consists of real-valued vectors. Our results show that for various situations of interest, this ``real'' classical shadow protocol improves the sample complexity over the standard scheme based on general Clifford unitaries. For example, when one is interested in estimating the expectation values of arbitrary real-valued observables, global orthogonal Cliffords decrease the required number of samples by a factor of two. More dramatically, for k-local observables composed only of real-valued Pauli operators, sampling local orthogonal Cliffords leads to a reduction by an exponential-in-k factor in the sample complexity over local unitary Cliffords. Finally, we show that by measuring in a basis containing complex-valued vectors, orthogonal shadows can, in the limit of large system size, exactly reproduce the original unitary shadows protocol.