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

International Conference on Control, Decision and Information Technologies (CoDIT)

On the Issue of an Anomaly Detection Algorithm for Identifying Potentially Generated Entangled Photons

Sergey Viktorovich Alekseyenko, Dmytro Babets, Kamil Wereszczyński, Krzysztof A. Cyran

Published Jul 15, 2025• 0 citations
Publisher

Abstract

Detecting entangled photons is critical for quantum technologies but challenging due to weak signals, noise, and rarity. This study proposes a fast, unsupervised anomaly detection algorithm using a multivariate Gaussian distribution to identify potential entangled photon events. Applied to data from spontaneous parametric down-conversion (SPDC) and silicon photomultiplier (SiMP) detectors, it successfully detected rare correlated events, offering a simple, efficient tool for quantum optics.

DOI
10.1109/codit66093.2025.11321854

Related papers

Isolation Forest as a Tool for Entangled Photon Detection

Dmytro Babets, Z. Opilski, Володимир Володимирович Гнатушенко +5 more

Entangled photon detection is essential for advancements in quantum communication, cryptography, and fundamental quantum mechanics experiments. This study introduces a novel application of unsupervised machine learning for identifying potential entangled photon events by analyzing voltage signals recorded from Silicon Multiplier Amplified Detectors (SiMPs). By framing photon detection as an anomaly detection problem, we employ the Isolation Forest (iForest) algorithm to isolate rare and distinctive signal patterns within large, noisy datasets without requiring labeled training data. This is the first application of iForest in the context of entangled photon detection. The method enables automated identification of anomalous events exhibiting time correlations across multiple measurement channels, offering a scalable and computationally efficient solution for real-time processing of experimental data in quantum optics.

International Conference on Control, Decision and Information Technologies (CoDIT)•Jul 15, 2025•1 citation

Sparsity-Driven Entanglement Detection in High-Dimensional Quantum States

Stav Lotan, Hugo Defienne, Ronen Talmon +1 more

The characterization of high-dimensional quantum entanglement is crucial for advanced quantum computing and quantum information algorithms. Traditional methods require extensive data acquisition and suffer from limited visibility due to experimental noise. Here, we introduce a sparsity-driven framework to enhance the detection and certification of high-dimensional entanglement in spatially entangled photon pairs. By applying $\ell_1$-regularized reconstruction to sample covariance matrices obtained from measurements on photons produced via spontaneous parametric down-conversion (SPDC) measurements, we enhance the visibility of the correlation signal while suppressing noise. We demonstrate, using a position-momentum Einstein-Podolsky-Rosen (EPR) entanglement criterion, that this approach enables certification of an entanglement dimensionality that cannot be achieved without regularization. Our method is scalable, simple to use and compatible with existing quantum-optics platforms, thus paves the way for efficient, real-time analysis of high-dimensional quantum states.

quant-pharXiv:2511.12546•Newton•Nov 18, 2025•0 citations

Real-time Processing of Correlated Photons for Quantum Imaging

Isaac Reichman, Gilad Pollack, Hadar Aharon +1 more

Quantum imaging leverages nonclassical correlations to reveal features that may vanish in conventional event integration [ 1, 2]. While single-photon detectors are well-established, quantum imaging demands complex data acquisition across many thousands of detectors, each generating rich metadata including triggering time, duration, and other properties. Consequently, effective data management, data wrangling, and correlation extraction become critical challenges. In this work, we investigate optimal handling of spatial correlations from a quantum-photonic source (spontaneous parametric down-conversion, SPDC) using an emerging single-photon event-based camera, the TPX3CAM (256×256 array, supported by an intensifier). Utilizing a femtosecond photon-pair source, we comprehensively analyse noise characteristics, including temporal dynamics and spatial distribution, to develop advanced spatio-temporal noise suppression strategies tailored to camera performance. Our primary objectives include real-time isolation and preprocessing of correlative data. Our analysis demonstrates the potential of these real-time correlation detection and noise reduction algorithms to expand quantum imaging capabilities across diverse scientific domains. Potential applications include high-resolution spectroscopy in biology, materials science, and advanced electron microscopy, where these techniques could enable identification of cascaded two-photon processes with atomic-scale precision. Figure 1 shows an excellent example of such a detection in real-space. The ring feature corresponds to the location of the correlated photon-pairs produced from the SPDC process. This is also apparent in figure 2, where we see a strong anti-correlation between the coordinates of the detected photons – corresponding to photon-pairs. <!-- named anchor --> Figure 1: Distribution of SPDC photons on TPX3CAM <!-- named anchor --> Figure 2: Spatial anti-correlation of x-coordinates

•Jan 1, 2025•0 citations

Photon statistics and spatial properties of photon pairs generated by spontaneous parametric down-conversion

Martin Hamar, Jan Peřina, Ondřej Haderka

Measurement of photon-number statistics of fields composed of photon pairs generated in spontaneous parametric down-conversion pumped by strong ultrashort pulses is described. Finite detection quantum efficiencies, noises as well as possible loss of one or both photons from a pair are taken into account. Measured data provided by an intensified CCD camera are analyzed along the developed model. The joint signal-idler photon-number distribution is obtained using the expectation maximization algorithm. Covariance of the signal and idler photon-numbers exceeds 98%. Entanglement area of the correlated photon pairs is determined.

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE•Aug 15, 2005•0 citations

Time-Correlated Photons from Spontaneous Parametric Down-Conversion (SPDC)

Aaron Dubois, Islam, Tanvir, , ‘26, Thapa Magar, Prashan, , ‘26

Time-correlated photons can be used to demonstrate emerging technical applications such as ghost imaging, and they can demonstrate quantum-based phenomena in order to interest undergraduate students in engineering applications involving optics and quantum mechanics. In this work, we use spontaneous parametric down-conversion to generate time-correlated photons. To achieve this, we simulated laboratory parameters with MATLAB and designed optical and electrical setups. Specifically, we used a 405 nm diode laser, various common optical components, and a nonlinear crystal designed to generate time-correlated photons, and we used single-photon detectors, time-to-amplitude converters, custom flash-conversion analog-to-digital converters, a Teensy 4.1 microcontroller, and the Arduino IDE to detect the time-correlated photons. We present two significant results. First, we measured >100,000 counts per second 5 ± 1° on either side of our input laser which was within our theoretical expectations of 4–9° and much greater than our < 2000 dark counts per second. Our second major result was finding 15.8 ± 0.7 time-correlated counts per second with an SNR of 2.7. We also outline future extensions of our work including ghost imaging and generating polarization-entangled photons.

•Apr 1, 2026•0 citations

Multi-qubit production in spontaneous parametric down conversion

Phillip Heitert, Finn Buldt, Pascal Bassène +1 more

We present a novel yet simple approach to produce multiple entangled photon pairs through spontaneous parametric down conversion (SPDC). We have designed Gaussian masks to subdivide the pump beam before passing it through a nonlinear medium. In this way, we are able to observe simultaneous separate down converted emission cones with spatial overlap. The technique we employ can be used to greatly increase the dimensionality of entangled photonic systems generated from spontaneous parametric down conversion, affording greater scalability to optical quantum computing than previously explored.

arXiv:2105.05291•Frontiers in Optics•Jan 1, 2021•1 citation
Detecting entangled photons is critical for quantum technologies but challenging due to weak signals, noise, and rarity. This study proposes a fast, unsupervised anomaly detection algorithm using a multivariate Gaussian distribution to identify potential entangled photon events. Applied to data from spontaneous parametric down-conversion (SPDC) and silicon photomultiplier (SiMP) detectors, it successfully detected rare correlated events, offering a simple, efficient tool for quantum optics.
Entangled photon detection is essential for advancements in quantum communication, cryptography, and fundamental quantum mechanics experiments. This study introduces a novel application of unsupervised machine learning for identifying potential entangled photon events by analyzing voltage signals recorded from Silicon Multiplier Amplified Detectors (SiMPs). By framing photon detection as an anomaly detection problem, we employ the Isolation Forest (iForest) algorithm to isolate rare and distinctive signal patterns within large, noisy datasets without requiring labeled training data. This is the first application of iForest in the context of entangled photon detection. The method enables automated identification of anomalous events exhibiting time correlations across multiple measurement channels, offering a scalable and computationally efficient solution for real-time processing of experimental data in quantum optics.
The characterization of high-dimensional quantum entanglement is crucial for advanced quantum computing and quantum information algorithms. Traditional methods require extensive data acquisition and suffer from limited visibility due to experimental noise. Here, we introduce a sparsity-driven framework to enhance the detection and certification of high-dimensional entanglement in spatially entangled photon pairs. By applying ℓ1\ell_1ℓ1​-regularized reconstruction to sample covariance matrices obtained from measurements on photons produced via spontaneous parametric down-conversion (SPDC) measurements, we enhance the visibility of the correlation signal while suppressing noise. We demonstrate, using a position-momentum Einstein-Podolsky-Rosen (EPR) entanglement criterion, that this approach enables certification of an entanglement dimensionality that cannot be achieved without regularization. Our method is scalable, simple to use and compatible with existing quantum-optics platforms, thus paves the way for efficient, real-time analysis of high-dimensional quantum states.
Quantum imaging leverages nonclassical correlations to reveal features that may vanish in conventional event integration [ 1, 2]. While single-photon detectors are well-established, quantum imaging demands complex data acquisition across many thousands of detectors, each generating rich metadata including triggering time, duration, and other properties. Consequently, effective data management, data wrangling, and correlation extraction become critical challenges. In this work, we investigate optimal handling of spatial correlations from a quantum-photonic source (spontaneous parametric down-conversion, SPDC) using an emerging single-photon event-based camera, the TPX3CAM (256×256 array, supported by an intensifier). Utilizing a femtosecond photon-pair source, we comprehensively analyse noise characteristics, including temporal dynamics and spatial distribution, to develop advanced spatio-temporal noise suppression strategies tailored to camera performance. Our primary objectives include real-time isolation and preprocessing of correlative data. Our analysis demonstrates the potential of these real-time correlation detection and noise reduction algorithms to expand quantum imaging capabilities across diverse scientific domains. Potential applications include high-resolution spectroscopy in biology, materials science, and advanced electron microscopy, where these techniques could enable identification of cascaded two-photon processes with atomic-scale precision. Figure 1 shows an excellent example of such a detection in real-space. The ring feature corresponds to the location of the correlated photon-pairs produced from the SPDC process. This is also apparent in figure 2, where we see a strong anti-correlation between the coordinates of the detected photons – corresponding to photon-pairs. <!-- named anchor --> Figure 1: Distribution of SPDC photons on TPX3CAM <!-- named anchor --> Figure 2: Spatial anti-correlation of x-coordinates
Measurement of photon-number statistics of fields composed of photon pairs generated in spontaneous parametric down-conversion pumped by strong ultrashort pulses is described. Finite detection quantum efficiencies, noises as well as possible loss of one or both photons from a pair are taken into account. Measured data provided by an intensified CCD camera are analyzed along the developed model. The joint signal-idler photon-number distribution is obtained using the expectation maximization algorithm. Covariance of the signal and idler photon-numbers exceeds 98%. Entanglement area of the correlated photon pairs is determined.
Time-correlated photons can be used to demonstrate emerging technical applications such as ghost imaging, and they can demonstrate quantum-based phenomena in order to interest undergraduate students in engineering applications involving optics and quantum mechanics. In this work, we use spontaneous parametric down-conversion to generate time-correlated photons. To achieve this, we simulated laboratory parameters with MATLAB and designed optical and electrical setups. Specifically, we used a 405 nm diode laser, various common optical components, and a nonlinear crystal designed to generate time-correlated photons, and we used single-photon detectors, time-to-amplitude converters, custom flash-conversion analog-to-digital converters, a Teensy 4.1 microcontroller, and the Arduino IDE to detect the time-correlated photons. We present two significant results. First, we measured >100,000 counts per second 5 ± 1° on either side of our input laser which was within our theoretical expectations of 4–9° and much greater than our < 2000 dark counts per second. Our second major result was finding 15.8 ± 0.7 time-correlated counts per second with an SNR of 2.7. We also outline future extensions of our work including ghost imaging and generating polarization-entangled photons.
We present a novel yet simple approach to produce multiple entangled photon pairs through spontaneous parametric down conversion (SPDC). We have designed Gaussian masks to subdivide the pump beam before passing it through a nonlinear medium. In this way, we are able to observe simultaneous separate down converted emission cones with spatial overlap. The technique we employ can be used to greatly increase the dimensionality of entangled photonic systems generated from spontaneous parametric down conversion, affording greater scalability to optical quantum computing than previously explored.