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

Isolation Forest as a Tool for Entangled Photon Detection

Dmytro Babets, Z. Opilski, Володимир Володимирович Гнатушенко, Віта Каштан, Agnieszka Michalczuk, Olena Sdvyzhkova, Erwin Maciak, Krzysztof A. Cyran

Published Jul 15, 2025• 1 citation

Publisher

Abstract

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.

DOI: 10.1109/codit66093.2025.11321541

Related papers

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

Sergey Viktorovich Alekseyenko, Dmytro Babets, Kamil Wereszczyński +1 more

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.

International Conference on Control, Decision and Information Technologies (CoDIT)Jul 15, 20250 citations

Quantum Machine Learning for Anomaly Detection

Jincheng Zhang

Anomaly detection, the task of identifying rare and unusual events within a dataset, is a critical component in numerous applications, including fraud detection, cybersecurity, and industrial monitoring. Traditional anomaly detection methods often rely on classical machine learning algorithms which can be sensitive to noise and may struggle with complex, high-dimensional data. This paper investigates the application of quantum machine learning (QML) algorithms to enhance anomaly detection performance. We propose a framework where anomaly detection problems are formulated as quantum machine learning tasks, leveraging quantum algorithms for data analysis and pattern recognition. The core claim of this work is that QML can significantly improve the precision and efficiency of anomaly detection compared to conventional techniques. Specifically, we explore the potential of quantum support vector machines (QSVMs) and quantum clustering algorithms for anomaly identification. The theoretical underpinnings of these approaches are outlined, and we present a conceptual model demonstrating the transformation of a classical anomaly detection problem into a quantum equivalent. This work lays the groundwork for future research in applying QML to real-world anomaly detection challenges.

Sep 5, 20260 citations

Quantum Anomaly Detection via Entanglement Decoherence in Distributed Systems

Jincheng Zhang

This paper proposes a novel approach to distributed anomaly detection leveraging the principles of quantum entanglement and decoherence. Traditional anomaly detection methods often struggle in distributed environments due to their centralized nature. This research introduces a system where a network of quantum sensors, governed by entanglement, is utilized to monitor and detect deviations from normal behavior. The core idea involves measuring the decoherence rates of entangled sensor pairs, which are intrinsically sensitive to disturbances and changes in the system. These decoherence rates are then processed through machine learning algorithms to classify events as normal or anomalous. The system's distributed architecture allows for real-time detection without relying on a central server, offering improved resilience and scalability. The theoretical framework and key components of this system are presented, highlighting the potential for significant advancements in anomaly detection across various distributed systems, including sensor networks, industrial control systems, and cybersecurity applications. The system's effectiveness relies on the precise quantification of quantum decoherence, which is a key challenge and a central focus of this work. We outline a methodology for analyzing the measured decoherence data and demonstrate the feasibility of utilizing this information for robust anomaly detection.

Aug 31, 20260 citations

基于量子机器学习的异常检测算法

Jincheng Zhang

This paper introduces a novel anomaly detection algorithm based on quantum machine learning. The algorithm leverages the unique properties of quantum mechanics to enhance the detection process, offering potential advantages over classical methods. We propose a quantum-enhanced clustering technique to identify anomalous data points. The core mechanism involves a quantum algorithm designed to effectively capture complex patterns within datasets. This approach aims to improve the accuracy and efficiency of anomaly detection, specifically by capitalizing on quantum phenomena such as superposition and entanglement. The paper details the algorithm's architecture, its quantum components, and the potential benefits it offers. We present preliminary results demonstrating the effectiveness of this algorithm in detecting various anomaly types. The core claim emphasizes the potential of quantum machine learning to revolutionize anomaly detection.

Aug 31, 20260 citations

Enhancing Quantum Operations: Random Forests for Anomaly Detection and Alert Generation

M. Anoop, B. Nithya

A promising approach for enhancing the dependability and security of quantum processes is the integration of Random Forests, a traditional machine learning technique, with quantum anomaly detection and alert production. This approach involves training Random Forest models to inspect quantum data and spot abnormalities such as variations in quantum states, entanglement patterns, and the results of quantum processes. When anomalies are found, alarms are activated, categorized by severity, and sent out via several notification channels. Operators of quantum systems can act quickly by applying techniques for adaptive control, error correction, or system reinitialization. The rigorous preservation of historical data makes post-incident study and ongoing system improvement possible. This novel strategy strengthens security while simultaneously improving the performance of quantum systems, particularly in situations involving quantum communication. To guarantee its efficiency in the continually changing quantum landscape, careful design and ongoing monitoring are essential.

International Conference on Circuit, Power and Computing TechnologiesAug 8, 20242 citations

Positive-unlabeled learning for training an entanglement detector

Taisei Nohara, Itsuki Noda, Satoshi Oyama

Abstract Entanglement detection, the process of verifying quantum entanglement is a fundamental challenge in quantum information processing. Various approaches have been proposed to address this challenge, with many recent studies applying supervised machine learning methods. While these methods have demonstrated high accuracy in entanglement detection, it is reasonable to assume that the entangled states themselves are not definitively known. To address this limitation, we have devised a machine learning method for entanglement detection based on positive-unlabeled learning, a classical machine learning framework that does not use label information from negative data. Using a deep neural network model to synthetic dataset under the assumption of mixed states, we conducted experiments on a classical computer to valid the effectiveness and characteristics of the proposed method. Our approach introduces a novel framework that accounts for the data generation constraints in the training process of entanglement detector, thereby advancing machine learning techniques in quantum information science.

Quantum Machine IntelligenceFeb 5, 20260 citations