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.

The game industry is wide and diverse, and the same can be said about the focus of games, as not all of them have entertainment in their core design. Many focus on pragmatic matters like education or science; such games are called serious games. One specific implementation of a serious game involves generating pseudo-random binary sequences (0 or 1) by observing the behavior of human players during gameplay for quantum research. Notably, the data collection process is often embedded subtly within the gameplay mechanics, potentially without the user’s explicit awareness. In our case, these humansourced bits are intended for use in quantum experiments testing Bell inequality, where unpredictable input plays a key role in addressing the freedom-of-choice loophole. The game serves as a covert interface between intuitive human decision-making and the controlled conditions of a quantum experiment. It is specifically designed to preserve the randomness and independence required for such foundational tests. This paper presents three serious game prototypes and explores how their core mechanics enable the extraction of behaviorally grounded binary sequences suitable for Bell experimental use as well as practicality of their deployment.

High temporal resolution is essential for applications in quantum optics, telecommunications, and biomedical research, yet traditional methods depend on costly high-frequency sampling systems. This paper introduces a novel, cost-effective approach to enhance temporal resolution in photon detection systems by integrating sequential time-shifted measurements with numerical integration. Unlike conventional techniques requiring expensive hardware, our method reconstructs high-resolution signals from low-rate measurements using standard photon detectors, such as Silicon Photomultipliers (SiMPs). The approach achieves sub-nanosecond precision by leveraging controlled time shifts and algorithmic processing, offering a scalable solution for quantum communication and biomedical applications. We demonstrate its effectiveness through a case study, showing a fivefold improvement in temporal resolution compared to baseline measurements, with minimal computational overhead. This method provides a practical alternative to high-cost systems, enhancing accessibility without compromising accuracy.

In this work, the authors present a comparative analysis of quantum computers in the context of image processing. The benchmark was performed on seven quantum computers and one simulator. For the real quantum devices, three different families of QPU (quantum processing units) were used: ion-trap devices and superconducting qubits (the IBM Falcon and Eagle series). To encode 585 images on the quantum computers, the FRQI (Flexible Representation of Quantum Images) and LPIQE (Local Phase Image Quantum Encoding) methods were employed. The reconstructed images were compared using the MSE (Mean Squared Error) and PCC (Pearson Correlation Coefficient) metrics. The experimental protocol was implemented in Python, using the Qiskit, SciPy, and Geqie libraries. The results of the experiment are presented in tabular form, as histograms, and as a correlation matrix.This study investigated the correlation of MSE with (i) the QPU implementation technology (i.e., between ion-trap devices and superconducting qubits) and (ii) the type of superconducting quantum processor (Falcon vs. Eagle). Additionally, differences in the error distributions between these QPU series (Falcon vs. Eagle) were analyzed.The main conclusions are as follows: a strong correlation of MSE with the type of superconducting quantum processor (Falcon vs. Eagle) and a moderate correlation of MSE with QPU implementation technology (ion-trap vs. superconducting) were demonstrated. Furthermore, it was shown that Falcon-series processors exhibit a bimodal error distribution, whereas Eagle processors exhibit a unimodal distribution. No correlation was found between MSE and the error rate.

The abstract nature of quantum mechanics presents significant challenges in education, often hindering students’ ability to understand fundamental concepts such as superposition, interference, and wave-particle duality. Traditional instructional approaches rely heavily on mathematical formalism, which can limit conceptual understanding. Augmented reality (AR) offers a promising pedagogical tool by providing an interactive and visually intuitive learning environment. This study examines the effectiveness of an AR-based visualization of the Mach-Zehnder Interferometer (MZI) in improving students’ comprehension of quantum optical phenomena.Our experimental study with 20 participants assessed the impact of AR-enhanced learning on conceptual understanding and practical application. Statistical analysis showed a significant improvement in post-interaction scores (t(19) = 4.88, p < 0.01), indicating a 45.45% increase in learning gains. In addition, 70% of participants successfully reconstructed the MZI setup, demonstrating improved spatial reasoning and understanding of quantum interference. Participants also reported reduced cognitive load and increased engagement compared to conventional methods.These findings highlight the pedagogical value of AR in quantum mechanics education, illustrating its potential to bridge the gap between theoretical abstraction and experiential learning. AR facilitates deeper conceptual engagement and retention by enabling real-time interaction with quantum components. This study underscores the role of immersive technologies in advancing science, technology, engineering, and mathematics (STEM) education. It suggests directions for future research to refine and expand AR-based instructional methodologies for broader implementation.

This study presents the Flexible Representation for Quantum Images (FRQI) Pairs method, a novel approach that leverages Quantum Recurrent Neural Networks (QRNN) for image classification. The proposed method achieves an accuracy of 74.60% on the full Modified National Institute of Standards and Technology (MNIST) handwritten digit data set, demonstrating its effectiveness in handling quantum encoded data for classification tasks.By reducing the size of the QRNN by the exponential factor, the FRQI Pairs method highlights the potential of integrating quantum computing principles with neural network architectures, offering a promising direction for advancing quantum machine learning.The research evaluates the FRQI Pairs method against existing quantum and classical models, demonstrating its competitive performance against other state-of-the-art approaches and showing potential for future advancements in the field. This research opens avenues for further exploration of quantum preprocessing and hybrid model architectures, marking a step forward in the application of quantum machine learning.

We present a conception of a novel quantum optical circuits designer, simulator, and visualization tool based on second quantization and the Fock space formalism. Our approach integrates a graph-based representation of quantum optical circuits with an augmented reality (AR) visualization system, enabling intuitive modeling and analysis. The framework ensures quantum correctness, supports real-time circuit generation, and bridges theoretical quantum optics with computational simulation, facilitating interactive quantum system design.

We report a successful results in the quantum image encoding - reconstruction experiments with the usage of local phase for pixel representation. This implementation was intended for using the photonic quantum system, since the phase shift is easy to produce. Our method will be implemented in real photonic quantum computers, after further development.Quantum Image Representation is well studied area in quantum informatics. The first proposed algorithm was the qubit-lattice method introduced in 2003. This method was not harnessing the full potential of quantum system - using as many qubits as there were pixel in the image.Most commonly used methods right now are Flexible Representation of Quantum Images (FRQI) proposed in 2011, and Novel Enhanced Quantum Representation (NEQR) proposed in 2013, which uses superposition to encode a monochrome image on a quantum computer using reduced number of qubits.In this paper we want to propose the combination of Local Phase Image Quantum Encoding method and Phase Distortion Unraveling (PDU) error mitigating method as an alternative method of quantum image encoding, producing satisfying results.

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming.

This paper reviews the recent development of augmented reality (AR) head-tracking techniques. AR is an emerging technology that provides users with digital augmentation layered on the real-life environment. AR systems use tracking to maintain the point of reference and enable the device to follow the user's movements and position within the physical world. Head-tracking is a crucial component of AR that inputs and interprets the movement of the device and the user's physical location in real-time. The main challenge in head-tracking is the appearance change that results from head rotation, which necessitates high accuracy and long-range tracking in a noisy environment. Latency and errors in head-tracking can reduce the system's overall effectiveness and cause jarring or unsettling effects for the user. This paper's contribution is to shed light on the best methods for tracking and registering in augmented reality, which will guide future advancements in the field. The review covers recent work proposed to improve head-tracking performance and reduce latency, highlighting the challenges and future directions of head-tracking techniques in AR.