Pith. sign in

REVIEW 1 cited by

A hybrid framework for estimating nonlinear functions of quantum states

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2208.08416 v2 pith:VIV4QEXI submitted 2022-08-17 quant-ph

classification quant-ph
keywords quantumframeworkhybridclassicalestimatingfunctionsmeasurementsnonlinear
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
abstract

Estimating nonlinear functions of quantum states, such as the moment $\tr(\rho^m)$, is of fundamental and practical interest in quantum science and technology. Here we show a quantum-classical hybrid framework to measure them, where the quantum part is constituted by the generalized swap test, and the classical part is realized by postprocessing the result from randomized measurements. This hybrid framework utilizes the partial coherent power of the intermediate-scale quantum processor and, at the same time, dramatically reduces the number of quantum measurements and the cost of classical postprocessing. We demonstrate the advantage of our framework in the tasks of state-moment estimation and quantum error mitigation.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Addressing the Readout Problem in Quantum Differential Equation Algorithms with Quantum Scientific Machine Learning

    quant-ph 2024-11 conditional novelty 5.0 of 10

    Quantum neural networks can classify shock and turbulent flow solutions encoded as quantum states, with accuracy strongly dependent on Fourier versus real-space basis choice.

Pith tools