Self-consistent quantum measurement tomography based on semidefinite programming

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Date

2023-07

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en

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14
1-14

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PHYSICAL REVIEW RESEARCH, Volume 5, issue 3

Abstract

We propose an estimation method for quantum measurement tomography (QMT) based on semidefinite programming (SDP) and discuss how it may be employed to detect experimental imperfections, such as shot noise and/or faulty preparation of the input states on near-term quantum computers. Moreover, if the positive operator-valued measure (POVM) we aim to characterize is informationally complete, we put forward a method for self-consistent tomography, i.e., for recovering a set of input states and POVM effects that is consistent with the experimental outcomes and does not assume any a priori knowledge about the input states of the tomography. Contrary to many methods that have been discussed in the literature, our approach does not rely on additional assumptions such as low noise or the existence of a reliable subset of input states.

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We would like to thank Laurin Fischer, Adam Glos, Francesco Tacchino, and Ivano Tavernelli for interesting discussions on noise detection on quantum hardware. We would also like to thank Carmen Vaccaro for preliminary studies on the runtime of the single-delta SDP, discussed in Appendix B. The SDPs presented in this work are integrated in AURORA, a proprietary quantum chemistry platform developed by Algorithmiq Ltd.

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Cattaneo , M , Rossi , M A C , Korhonen , K , Borrelli , E M , García-Pérez , G , Zimborás , Z & Cavalcanti , D 2023 , ' Self-consistent quantum measurement tomography based on semidefinite programming ' , PHYSICAL REVIEW RESEARCH , vol. 5 , no. 3 , 033154 , pp. 1-14 . https://doi.org/10.1103/PhysRevResearch.5.033154