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

Photonics North (PN)

Emergence of biased errors in imperfect photonic circuits

Fulvio Flamini

Published May 31, 2021• 1 citation
Publisher

Abstract

We numerically investigate the impact of imperfections and biased errors in integrated photonic circuits. Our analysis connects and deepens previous studies along the same direction, revealing potential criticalities for high-precision tests and optical implementations of machine learning.
DOI
10.1109/pn52152.2021.9597850

Related papers

Emergence of Biased Errors in Imperfect Optical Circuits

Fulvio Flamini

We study the impact of experimental imperfections in integrated optical circuits. We discuss the emergence of a moderate biased error in path encoding, and investigate its correlation with properties of the optical paths. Our analysis connects and deepens previous studies in this direction, revealing potential issues in high-precision tests and optical implementations of machine learning.

arXiv:2106.06717•Physical Review Applied•Dec 15, 2021•0 citations

Emergence of biased errors in imperfect photonic circuits

Fulvio Flamini

This notebook allows to reproduce numerical results in the manuscript Emergence of biased errors in imperfect photonic circuits. It includes ready-to-use functions to study error propagation in photonic circuits implementing universal architectures. Additional analyses on the properties of optical paths are provided.

•Jun 11, 2021•0 citations

Handling Imperfections in Subcircuit Compilation for Linear Optical Quantum Computing

Tobias Forster, Yannick Stade, Andreas Fyrillas +3 more

Linear Optical Quantum Computing (LOQC) is emerging as a promising technology in large-scale quantum computing. Corresponding devices, including linear optical circuits, are rapidly growing in size and fabrication precision. However, even with improved quality, the circuits still exhibit imperfections leading to photon loss or phase noise that vary across the circuit. Already small deficiencies can have a vast impact on both accuracy and efficiency. For computations that do not require the entire linear optical circuit, so-called subcircuits, one can choose where to perform the computation, opening optimization opportunities that are addressed by subcircuit compilation. Current compilers spread the subcircuit's computation across the entire circuit, connecting the same input and output ports, thereby failing to fully exploit the optimization opportunities. However, given the subcircuit's reduced size compared to the entire circuit, one can keep its implementation localized and intentionally route photons to connect good input and output ports. This, however, is a highly non-trivial task, as numerous options must be weighed against one another, and the quality of the solution is influenced by a wide range of hardware parameters. In particular, the reflectivities of imperfect beam splitters can lead to photon losses during routing, which must be traded off against losses at the input and output ports due to imperfect transmission rates. Since current compilers do not account for these effects, we propose the first efficient subcircuit compilation method that optimizes for the coincidence rate, the probability of successfully measuring all desired photons, by handling hardware imperfections. Our evaluations demonstrate significant improvement over existing methods in robustness against phase noise and significantly increased coincidence rates across all benchmarks of up to plus 33.2%.

quant-pharXiv:2609.13379•Sep 15, 2026•0 citations

High-Fidelity Quantum Information Processing with Machine Learning-Characterized Photonic Circuits

Andreas Fyrillas, Olivier Faure, Nicolas Maring +2 more

Photonic integrated circuits (PICs) are attractive platforms for manipulating quantum light. Imperfections limit the fidelity of photonically integrated quantum information protocols. We use machine learning and a clear box approach to characterize large PICs and mitigate imperfections, achieving high-fidelity for scalable implementations.

Quantum 2.0 Conference and Exhibition•Jan 1, 2024•0 citations

Errors and losses impact on planar integrated photonic circuits fidelity

Wilder R. Cardoso

The European Physical Journal Plus•Mar 21, 2024•0 citations

Reconfigurable integrated photonic circuits for quantum machine learning

Alberto Beccari

•Oct 3, 2017•0 citations
Linear Optical Quantum Computing (LOQC) is emerging as a promising technology in large-scale quantum computing. Corresponding devices, including linear optical circuits, are rapidly growing in size and fabrication precision. However, even with improved quality, the circuits still exhibit imperfections leading to photon loss or phase noise that vary across the circuit. Already small deficiencies can have a vast impact on both accuracy and efficiency. For computations that do not require the entire linear optical circuit, so-called subcircuits, one can choose where to perform the computation, opening optimization opportunities that are addressed by subcircuit compilation. Current compilers spread the subcircuit's computation across the entire circuit, connecting the same input and output ports, thereby failing to fully exploit the optimization opportunities. However, given the subcircuit's reduced size compared to the entire circuit, one can keep its implementation localized and intentionally route photons to connect good input and output ports. This, however, is a highly non-trivial task, as numerous options must be weighed against one another, and the quality of the solution is influenced by a wide range of hardware parameters. In particular, the reflectivities of imperfect beam splitters can lead to photon losses during routing, which must be traded off against losses at the input and output ports due to imperfect transmission rates. Since current compilers do not account for these effects, we propose the first efficient subcircuit compilation method that optimizes for the coincidence rate, the probability of successfully measuring all desired photons, by handling hardware imperfections. Our evaluations demonstrate significant improvement over existing methods in robustness against phase noise and significantly increased coincidence rates across all benchmarks of up to plus 33.2%.