One intriguing but difficult route to scalable quantum information processing (QIP) is linear optical quantum computing (LOQC). The systematic growth of LOQC is examined in this research. It focuses on significant experimental developments, fault-tolerant methods, and the enduring difficulties influencing the field area. In addition, it highlights the incorporation of Gottesman-Kitaev-Preskill (GKP) encoding which is an essential step towards fault-tolerant quantum computation. We analyze the importance of the Knill-Laflamme-Milburn (KLM) protocol in addressing the probabilistic limits of two-qubit gates. Strong quantum structures are made possible by this synergy, which increases resistance to phase errors and photon loss. Notwithstanding these developments, problems with state preparation, measurement accuracy, and error correction still exist, requiring multidisciplinary efforts to improve methods and investigate fresh ideas.Beyond its scope, the knowledge gathered from LOQC may have an impact on secure quantum communication networks and hybrid quantum systems. Enhancing error correction codes, creating new quantum optical technologies, and encouraging cooperation within quantum computing paradigms should be the main goals of future research. The present work intends to stimulate more developments and push QIP towards scalable and useful quantum computation by reviewing the state of LOQC today and defining strategic directions.

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 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.