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 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.

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.

Quantum Image Representation is a 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 the quantum system - using as many qubits as there were pixels in the image. The 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 a reduced number of qubits. In this paper, we want to compare these two methods, alongside Local Phase Image Quantum Encoding (LPIQE) method proposed in 2020, to see which method produces images with minimal data loss. Another comparison will be done with image encoded using the LPIQE method with Phase Distortion Unraveling (PDU) error mitigation method, which is done entirely on a classical computer. LPIQE implementation was intended for using the photonic quantum system since the phase-shift is easy to produce, and it will be implemented on real, photonic quantum computers after further development.

We introduce synchronized and calibrated multi-view video and motion capture dataset for motion analysis and gait identification. The 3D gait dataset consists of 166 data sequences with 32 people. In 128 data sequences, each of 32 individuals was dressed in his/her clothes, in 24 data sequences, 6 of 32 performers changed clothes, and in 14 data sequences, 7 of the performers had a backpack on his/her back. In a single recording session, every performer walked from right to left, then from left to right, and afterwards on the diagonal from upper-right to bottom-left and from bottom-left to upper-right corner of a rectangular scene. We demonstrate that a baseline algorithm achieves promising results in a challenging scenario, in which gallery/training data were collected in walks perpendicular/facing to the cameras, whereas the probe/testing data were collected in diagonal walks. We compare performances of biometric gait recognition that were achieved on marker-less and marker-based 3D data. We present recognition performances, which were achieved by a convolutional neural network and classic classifiers operating on gait signatures obtained by multilinear principal component analysis. The availability of synchronized multi-view image sequences with 3D locations of body markers creates a number of possibilities for extraction of discriminative gait signatures. The gait data are available at http://bytom.pja.edu.pl/projekty/hm-gpjatk/.

The aim of this study was to analyze the presence of chaotic behaviors in gait of elderly subjects (`65+') by means of the largest short-term Lyapunov exponent computed for six scenarios which differed with respect to ground inclination, walking speed and optional external perturbation. The largest Lyapunov exponent quantifies the amount of chaos in a time series. The bigger positive Lyapunov exponent value, the greater system's sensitivity to infinitesimal changes in initial conditions at the beginnings of successive gait cycles and, thus, a chaotic behavior during walking. The time series representing the joint angles of ankle, knee and hip joints were recorded in the CAREN Extended environment using the motion capture technique. Data collected for respective joints were grouped forming for each joint a multivariate time series - input data for calculation of the largest Lyapunov exponent.

Human gait is the result of complex interplay of the nervous, musculoskeletal and cardiorespiratory systems. Unimpaired cognition and executive control are needed for safe walking. Balance and gait problems may be precursors of falls, which are the leading cause of elderly injuries. This article concerns using the Corridor application, run on the multi-sensory virtual reality system (CAREN, Computer Assisted Rehabilitation ENvironment) to evaluate chosen gait parameters of elderly participants. The presented application influences the conditions of postural stability by skewing the image of space in different directions, especially side to side. This provoking fall prevention strategy can be described by set of changed gait parameters. Differences are observed in the hip and ankle joints, spatio-temporal parameters and the results that differed during the gait with dual-task. To better evaluate the gait asymmetry the arm swing asymmetry and step length symmetry indices were used.

We present an algorithm for view-independent gait-based person identification. The identification is achieved using data obtained by our marker-less 3D motion tracking algorithm. The motion tracking was accomplished by a particle swarm optimization algorithm. The accuracy of the motion tracking algorithm was evaluated using ground-truth data from MoCap. It was determined on 88 sequences with 22 walking performers. We obtained 90% identification accuracy (rank 1) on 230 gait cycles.