Self-consistent quantum measurement tomography based on semidefinite programming

Cattaneo, Marco; Rossi, Matteo A. C.; Korhonen, Keijo; Borrelli, Elsi-Mari; García-Pérez, Guillermo; Zimborás, Zoltán; Cavalcanti, Daniel

doi:10.1103/physrevresearch.5.033154

Phys. Rev. Research

2023

DOI: 10.1103/physrevresearch.5.033154

|View full text |Cite

Self-consistent quantum measurement tomography based on semidefinite programming

Marco Cattaneo,

Matteo A. C. Rossi,

Keijo Korhonen

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article4

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Calibration of syndrome measurements in a single experiment

Wimmer,

Szangolies,

Epping

2024

New J. Phys.

View full text Add to dashboard Cite

Quantum error correction can reduce the effects of noise in quantum systems, e.g. in metrology or most notably in quantum computing. Typically, this requires making measurements that provide information about the errors that have occurred in the system. However, these syndrome measurements themselves introduce noise into the system, for example by using noisy gates. A complete characterization of the measurements is very costly. Here we describe a calibration method to obtain the syndrome statistics taking into account the additional noise sources. All calibration data is extracted from a single experiment in which the syndrome measurement is performed twice in a row. Thus, our method allows an accurate evaluation of syndrome measurements with significantly less effort than existing methods. We give examples of the application of this method to noise estimation and error correction. Finally, we discuss the results of experiments performed on an IBM quantum computer.

show abstract

Calibration of syndrome measurements in a single experiment

Wimmer,

Szangolies,

Epping

2024

New J. Phys.

View full text Add to dashboard Cite

show abstract

Tensor network noise characterization for near-term quantum computers

Mangini,

Cattaneo,

Cavalcanti

et al. 2024

Phys. Rev. Research

View full text Add to dashboard Cite

Characterization of noise in current near-term quantum devices is of paramount importance to fully use their computational power. However, direct quantum process tomography becomes unfeasible for systems composed of tens of qubits. A promising alternative method based on tensor networks was recently proposed []. In this paper, we adapt it for the characterization of noise channels on near-term quantum computers and investigate its performance thoroughly. In particular, we show how experimentally feasible tomographic samples are sufficient to accurately characterize realistic correlated noise models affecting individual layers of quantum circuits, and study its performance on systems composed of up to 20 qubits. Furthermore, we combine this noise characterization method with a recently proposed noise-aware tensor network error mitigation protocol for correcting outcomes in noisy circuits, resulting accurate estimations even on deep circuit instances. This positions the tensor-network-based noise characterization protocol as a valuable tool for practical error characterization and mitigation in the near-term quantum computing era. Published by the American Physical Society 2024

show abstract

Enhancing quantum state tomography via resource-efficient attention-based neural networks

Palmieri,

Müller-Rigat,

Srivastava

et al. 2024

Phys. Rev. Research

View full text Add to dashboard Cite

In this paper, we propose a method for denoising experimental density matrices that combines standard quantum state tomography with an attention-based neural network architecture. The algorithm learns the noise from the data itself, without knowledge of its sources. Firstly, we show how the proposed protocol can improve the averaged fidelity of reconstruction over linear inversion and maximum likelihood estimation in the finite-statistics regime, reducing at least by an order of magnitude the amount of necessary training data. Next, we demonstrate its use for out-of-distribution data in realistic scenarios. In particular, we consider squeezed states of few spins in the presence of depolarizing noise and measurement/calibration errors and certify its metrologically useful entanglement content. The protocol introduced here targets experiments involving few degrees of freedom and afflicted by a significant amount of unspecified noise. These include NISQ devices and platforms such as trapped ions or photonic qudits. Published by the American Physical Society 2024

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Self-consistent quantum measurement tomography based on semidefinite programming

Cited by 4 publications

References 50 publications

Calibration of syndrome measurements in a single experiment

Calibration of syndrome measurements in a single experiment

Tensor network noise characterization for near-term quantum computers

Enhancing quantum state tomography via resource-efficient attention-based neural networks

Contact Info

Product

Resources

About