Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Quantum 2.0 Conference and Exhibition

Evaluating cyclic translational symmetry in boson sampling systems with few detectors

Tudor-Alexandru Isdrailă, Jun-Yi Wu

Published Jan 1, 2024• 0 citations
Publisher

Abstract

Tomography of multi-photon states represents a complex procedure requiring a large number of photon number resolving detectors. By expanding an efficient single-photon tomography protocol we recover a reduced density matrix with just two detectors.
DOI
10.1364/quantum.2024.qtu3a.5

Related papers

Tomographic characterization of three-qubit pure states with only two-qubit detectors

Daniel Cavalcanti, Leandro M. Cioletti, Marcelo Terra Cunha

A tomographic process for three-qubit pure states using only pairwise detections is presented.

arXiv:quant-ph/0408022•Physical Review A•Jan 3, 2005•6 citations

Optimized detector tomography for photon-number-resolving detectors with hundreds of pixels

D. Liu, Jia-Qi Wang, Chang‐Ling Zou +2 more

Photon-number-resolving detectors with hundreds of pixels are now readily available, while the characterization of these detectors using detector tomography is computationally intensive. Here, we present a modified detector tomography model that reduces the number of variables that need optimization. To evaluate the effectiveness and accuracy of our model, we reconstruct the photon number distribution of optical coherent and thermal states using the expectation-maximization-entropy algorithm. Our results indicate that the fidelity of the reconstructed states remains above 99%, and the second- and third-order correlations agree well with the theoretical values for a mean number of photons up to 100. We also investigate the computational resources required for detector tomography and find out that our approach reduces the solving time by around half compared to the standard detector tomography approach, and the required memory resources are the main obstacle for detector tomography of a large number of pixels. Our results suggest that detector tomography is viable on a supercomputer with 1 TB RAM for detectors with up to 340 pixels.

arXiv:2306.12622•Physical Review A•Nov 20, 2023•5 citations

Quantum Software Models: Quantum Modules Tomography and Recovery Theorem

Iaakov Exman, Ariel Zvulunov

Quantum Tomography partially measures and then recovers the remaining density matrix quantum state, in order to verify that a certain device -processor or detector -indeed outputs the intended quantum state.However, single matrix element measurements rapidly increase in number with the density matrix dimension and are error-prone.This work proposes a novel quantum software viewpoint on quantum tomography.Instead of individual matrix elements, measurements and density matrix recovery are performed on higher-abstraction modules, i.e. semantically meaningful groups of matrix elements.Quantum Modules Tomography potentially reduce the necessary number of explicit measurements, while still allowing recovery of the whole density matrix.A Recovery Theorem is formulated and proved: density matrix diagonal measurements suffice to recover the whole system density matrix, in terms of modules.The recovery procedure is illustrated by a few case studies.

Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering•Jul 1, 2023•2 citations

Efficient reconstruction of density matrices for high dimensional quantum state tomography

Jiaojiao Zhang, Kezhi Li, Shuang Cong +1 more

Signal Processing•Apr 13, 2017•19 citations

Staking out the physical sectors of Hilbert spaces

Dmitri S. Mogilevtsev, Yong Siah Teo, Jaroslav Řeháček +6 more

We propose an efficient procedure to confidently identify the subspace that contains all the relevant information about a quantum state that lives in an arbitrarily large-dimensional Hilbert space---its physical sector. This entails only a few simple measurements and does not require performing a full tomography or any additional \emph{ad hoc} assumption. We apply this approach to both discrete- and continuous-variable systems. For the former, this turns out to be a suitable deterministic dimension-testing protocol. For the latter, we experimentally demonstrate the method without the need for ideal photon-number-resolving detectors.

•Sep 9, 2016•0 citations

Efficient tomography and entanglement detection of multiphoton states

Christian Schwemmer

•Jan 1, 2015•0 citations
Photon-number-resolving detectors with hundreds of pixels are now readily available, while the characterization of these detectors using detector tomography is computationally intensive. Here, we present a modified detector tomography model that reduces the number of variables that need optimization. To evaluate the effectiveness and accuracy of our model, we reconstruct the photon number distribution of optical coherent and thermal states using the expectation-maximization-entropy algorithm. Our results indicate that the fidelity of the reconstructed states remains above 99%, and the second- and third-order correlations agree well with the theoretical values for a mean number of photons up to 100. We also investigate the computational resources required for detector tomography and find out that our approach reduces the solving time by around half compared to the standard detector tomography approach, and the required memory resources are the main obstacle for detector tomography of a large number of pixels. Our results suggest that detector tomography is viable on a supercomputer with 1 TB RAM for detectors with up to 340 pixels.
Quantum Tomography partially measures and then recovers the remaining density matrix quantum state, in order to verify that a certain device -processor or detector -indeed outputs the intended quantum state.However, single matrix element measurements rapidly increase in number with the density matrix dimension and are error-prone.This work proposes a novel quantum software viewpoint on quantum tomography.Instead of individual matrix elements, measurements and density matrix recovery are performed on higher-abstraction modules, i.e. semantically meaningful groups of matrix elements.Quantum Modules Tomography potentially reduce the necessary number of explicit measurements, while still allowing recovery of the whole density matrix.A Recovery Theorem is formulated and proved: density matrix diagonal measurements suffice to recover the whole system density matrix, in terms of modules.The recovery procedure is illustrated by a few case studies.
We propose an efficient procedure to confidently identify the subspace that contains all the relevant information about a quantum state that lives in an arbitrarily large-dimensional Hilbert space---its physical sector. This entails only a few simple measurements and does not require performing a full tomography or any additional \emph{ad hoc} assumption. We apply this approach to both discrete- and continuous-variable systems. For the former, this turns out to be a suitable deterministic dimension-testing protocol. For the latter, we experimentally demonstrate the method without the need for ideal photon-number-resolving detectors.