Search Results (232)

Immersive environments have brought a great transformation in human–computer interaction by enabling realistic and interactive experiences within simulated or augmented spaces. In these immersive environments, virtual assets such as custom avatars, digital artwork, and virtual real estate play an important role, often holding a substantial value in both virtual and real worlds. However, this value also makes them attractive to fraudulent activities. As a result, ensuring the authenticity and integrity of virtual assets is of concern. This study proposes a cryptographic solution that leverages digital signatures and hash algorithms to secure virtual assets in immersive environments. The system employs RSA-2048 for signing and SHA-256 hashing for binding the digital signature to the asset’s data to prevent tampering and forgery. Our experimental evaluation demonstrates that the signing process operates with remarkable efficiency; over ten trials, the signing time averaged 17.3 ms, with a narrow range of 16–19 ms and a standard deviation of 1.1 ms. Verification times were near-instantaneous (0–1 ms), ensuring real-time responsiveness. Moreover, the signing process incurred a minimal memory footprint of approximately 4 KB, highlighting the system’s suitability for resource-constrained VR applications. Simulations of tampering and forgery attacks further validated the system’s capability to detect unauthorized modifications, with a 100% detection rate observed across multiple trials. While the system currently employs RSA, which may be vulnerable to quantum computing in the future, its modular design ensures crypto-agility, allowing for the integration of quantum-resistant algorithms as needed. This work not only addresses immediate security challenges in immersive environments but also lays the groundwork for broader applications, including regulatory compliance for financial virtual assets. Full article

Quantum chemistry offers the formal machinery to derive molecular and physical properties arising from (sub)atomic interactions. However, as molecules of practical interest are largely polyatomic, contemporary approximation schemes such as the Hartree–Fock scheme are computationally expensive due to the large number of electron repulsion integrals (ERIs). Central to the Hartree–Fock method is the efficient computation of ERIs over Gaussian functions (GTO-ERIs). Here, the well-known McMurchie–Davidson method (MD) offers an elegant formalism by incrementally extending Hermite Gaussian functions and auxiliary tabulated functions. Although the MD method offers a high degree of versatility to acceleration schemes through Graphics Processing Units (GPUs), the current GPU implementations limit the practical use of supported values of the azimuthal quantum number. In this paper, we propose a generalized framework capable of computing GTO-ERIs for arbitrary azimuthal quantum numbers, provided that the intermediate terms of the MD method can be stored. Our approach benefits from extending the MD recurrence relations through shells, batches, and triple-buffering of the shared memory, and ordering similar ERIs, thus enabling the effective parallelization and use of GPU resources. Furthermore, our approach proposes four GPU implementation schemes considering the suitable mappings between Gaussian basis and CUDA blocks and threads. Our computational experiments involving the GTO-ERI computations of molecules of interest on an NVIDIA A100 Tensor Core GPU (NVIDIA, Santa Clara, CA, USA) have revealed the merits of the proposed acceleration schemes in terms of computation time, including up to a 72× improvement over our previous GPU implementation and up to a 4500× speedup compared to a naive CPU implementation, highlighting the effectiveness of our method in accelerating ERI computations for both monatomic and polyatomic molecules. Our work has the potential to explore new parallelization schemes of distinct and complex computation paths involved in ERI computation. Full article

Collision models have attracted significant attention in recent years due to their versatility to simulate open quantum systems in different dynamical regimes. They have been used to study various interesting phenomena such as the dynamical emergence of non-Markovian memory effects and the spontaneous establishment of synchronization in open quantum systems. In such models, the repeated pairwise interactions between the system and the environment and also the possible coupling between different environmental units are typically modeled using the coherent partial SWAP (PSWAP) operation as it is known to be a universal homogenizer. In this study, we investigate the dynamical behavior of incoherent collision models, where the interactions between different units are modeled by the incoherent controlled SWAP (CSWAP) operation, which is also a universal homogenizer. Even though the asymptotic dynamics of the open system in cases of both coherent and incoherent swap interactions appear to be identical, its transient dynamics turns out to be significantly different. Here, we present a comparative analysis of the consequences of having coherent or incoherent couplings in collision models, namely, PSWAP or CSWAP interactions, respectively, for the emergence of memory effects for a single-qubit system and for the onset synchronization between a pair of qubits, both of which are strictly determined by the transient dynamics of the open system. Full article

Fault location in DC microgrids (DCMGs) is a critical challenge due to the system’s inherent complexities and the demand for high reliability in modern power systems. This study proposes an explainable artificial intelligence (XAI)-based quantum deep neural network (QDNN) framework to address fault localization challenges in DCMGs. First, voltage signals from the DCMG are collected and analyzed using high-order synchrosqueezing transform to detect traveling waves (TWs) and extract critical fault parameters such as time of arrival, magnitude, and polarity of the first and second TWs. These features are fed into the proposed QDNN model that integrates advanced learning techniques for accurate fault localization. The cumulative distance from the fault point to the bus connecting the DCMG to the power network is considered the output vector. The model uses a combination of deep learning and quantum computing techniques to extract features and improve accuracy. To ensure transparency, an XAI technique called Shapley additive explanations (SHAP) is applied, enabling system operators to identify critical fault features. The SHAP-based explainability framework plays a critical role in translating the model’s predictions into actionable insights, ensuring that the proposed solution is not only accurate but also practically implementable in real-world scenarios. The results demonstrate the QDNN framework’s superior accuracy in fault localization even in noisy environments and with high-resistance faults, independent of voltage levels and DCMG configurations, making it a robust solution for modern power systems. Full article

About twelve years ago, the use of standard functional manipulations was demonstrated to imply an unexpected property satisfied by the fermionic Green’s functions of $QCD$. This non-perturbative phenomenon has been dubbed an effective locality. In a much simpler way than in $QCD$, the most remarkable and intriguing aspects of effective locality have been presented in a recent publication on the Yang-Mills theory on Minkowski spacetime. While quickly recalled in the current paper, these results are used to calculate the problematic gluonic propagator in the Yang-Mills non-perturbative regime. This paper is dedicated to the memory of Professor Herbert M. Fried (1929–2023), whose inspiring manner, impressive command of functional methods in quantum field theories, enthusiasm for a broad range of topics in Theoretical Physics, and warm friendship are missed greatly by the authors. Full article

The Internet of Things (IoT), which is characteristic of the current industrial revolutions, is the connection of physical devices through different protocols and sensors to share information. Even though the IoT provides revolutionary opportunities, its connection to the current Internet for smart cities brings new opportunities for security threats, especially with the appearance of new threats like quantum computing. Current approaches to protect IoT data are not immune to quantum attacks and are not designed to offer the best data management for smart city applications. Thus, post-quantum cryptography (PQC), which is still in its research stage, aims to solve these problems. To this end, this research introduces the Dynamic Galois Reed–Solomon with Quantum Resilience (DGRS-QR) system to improve the secure management and communication of data in IoT smart cities. The data preprocessing includes K-Nearest Neighbors (KNN) and min–max normalization and then applying the Galois Field Adaptive Expansion (GFAE). Optimization of the quantum-resistant keys is accomplished by applying Artificial Bee Colony (ABC) and Moth Flame Optimization (MFO) algorithms. Also, role-based access control provides strong cloud data security, and quantum resistance is maintained by refreshing keys every five minutes of the active session. For error correction, Reed–Solomon (RS) codes are used which provide data reliability. Data management is performed using an attention-based Bidirectional Long Short-Term Memory (Att-Bi-LSTM) model with skip connections to provide optimized city management. The proposed approach was evaluated using key performance metrics: a key generation time of 2.34 s, encryption time of 4.56 s, decryption time of 3.56 s, PSNR of 33 dB, and SSIM of 0.99. The results show that the proposed system is capable of protecting IoT data from quantum threats while also ensuring optimal data management and processing. Full article

We extend the Quantum Memory Matrix (QMM) framework, originally developed to reconcile quantum mechanics and general relativity by treating space–time as a dynamic information reservoir, to incorporate the full suite of Standard Model gauge interactions. In this discretized, Planck-scale formulation, each space–time cell possesses a finite-dimensional Hilbert space that acts as a local memory, or quantum imprint, for matter and gauge field configurations. We focus on embedding non-Abelian SU(3)_c (quantum chromodynamics) and SU(2)_L × U(1)_Y (electroweak interactions) into QMM by constructing gauge-invariant imprint operators for quarks, gluons, electroweak bosons, and the Higgs mechanism. This unified approach naturally enforces unitarity by allowing black hole horizons, or any high-curvature region, to store and later retrieve quantum information about color and electroweak charges, thereby preserving subtle non-thermal correlations in evaporation processes. Moreover, the discretized nature of QMM imposes a Planck-scale cutoff, potentially taming UV divergences and modifying running couplings at trans-Planckian energies. We outline major challenges, such as the precise formulation of non-Abelian imprint operators and the integration of QMM with loop quantum gravity, as well as possible observational strategies—ranging from rare decay channels to primordial black hole evaporation spectra—that could provide indirect probes of this discrete, memory-based view of quantum gravity and the Standard Model. Full article

We studied relaxation processes in heavily doped two-dimensional Dirac systems associated with electron scattering by acoustic and optical phonons and by static disorder. The frequency dependence of the real and imaginary parts of the relaxation function is calculated for different temperatures. The two-component low-frequency dynamical conductivity is found to be strongly dependent on temperature. At low temperatures, the imaginary part of the zero-frequency relaxation function and the DC resistivity are characterized by the scaling law $a{T}^x$ with the exponent x between 2.5 and 3. Full article

Tensor networks are a very powerful data structure tool originating from simulations of quantum systems. In recent years, they have seen increased use in machine learning, mostly in trainings with gradient-based techniques, due to their flexibility and performance achieved by exploiting hardware acceleration. As ansatzes, tensor networks can be used with flexible geometries, and it is known that for highly regular ones, their dimensionality has a large impact on performance and representation power. For heterogeneous structures, however, these effects are not completely characterized. In this article, we train tensor networks with different geometries to encode a random quantum state, and see that densely connected structures achieve better infidelities than more sparse structures, with higher success rates and less time. Additionally, we give some general insight on how to improve the memory requirements of these sparse structures and the impact of such improvement on the trainings. Finally, as we use HPC resources for the calculations, we discuss the requirements for this approach and showcase performance improvements with GPU acceleration on a last-generation supercomputer. Full article

This study presents a method for the active control of a follow-up lower extremity exoskeleton rehabilitation robot (LEERR) based on human motion intention recognition. Initially, to effectively support body weight and compensate for the vertical movement of the human center of mass, a vision-driven follow-and-track control strategy is proposed. Subsequently, an algorithm for recognizing human motion intentions based on machine learning is proposed for human-robot collaboration tasks. A muscle–machine interface is constructed using a bi-directional long short-term memory (BiLSTM) network, which decodes multichannel surface electromyography (sEMG) signals into flexion and extension angles of the hip and knee joints in the sagittal plane. The hyperparameters of the BiLSTM network are optimized using the quantum-behaved particle swarm optimization (QPSO) algorithm, resulting in a QPSO-BiLSTM hybrid model that enables continuous real-time estimation of human motion intentions. Further, to address the uncertain nonlinear dynamics of the wearer-exoskeleton robot system, a dual radial basis function neural network adaptive sliding mode Controller (DRBFNNASMC) is designed to generate control torques, thereby enabling the precise tracking of motion trajectories generated by the muscle–machine interface. Experimental results indicate that the follow-up-assisted frame can accurately track human motion trajectories. The QPSO-BiLSTM network outperforms traditional BiLSTM and PSO-BiLSTM networks in predicting continuous lower limb motion, while the DRBFNNASMC controller demonstrates superior gait tracking performance compared to the fuzzy compensated adaptive sliding mode control (FCASMC) algorithm and the traditional proportional–integral–derivative (PID) control algorithm. Full article

Quantum dot (QD)-based single-photon emitter devices today are based on self-assembled random position nucleated QDs emitting at random wavelengths. Deterministic QD growth in position and emitter wavelength would be highly appreciated for industry-scale high-yield device manufacturing from wafers. Local droplet etching during molecular beam epitaxy is an all in situ method that allows excellent density control and predetermines the nucleation site of quantum dots. This method can produce strain-free GaAs QDs with excellent photonic and spin properties. Here, we focus on the emitter wavelength homogeneity. By wafer rotation-synchronized shutter opening time and adapted growth parameters, we grow QDs with a narrow peak emission wavelength homogeneity with no more than 1.2 nm shifts on a 45 mm diameter area and a narrow inhomogeneous ensemble broadening of only 2 nm at 4 K. The emission wavelength of these strain-free GaAs QDs is <800 nm, attractive for quantum optics experiments and quantum memory applications. We can use a similar random local droplet nucleation, nanohole drilling, and now, InAs infilling to produce QDs emitting in the telecommunication optical fiber transparency window around 1.3 µm, the so-called O-band. For this approach, we demonstrate good wavelength homogeneity and excellent density homogeneity beyond the possibilities of standard Stranski–Krastanov self-assembly. We discuss our methodology, structural and optical properties, and limitations set by our current setup capabilities. Full article

Quantum reservoir computing (QRC) has emerged as a promising paradigm for harnessing near-term quantum devices to tackle temporal machine learning tasks. Yet, identifying the mechanisms that underlie enhanced performance remains challenging, particularly in many-body open systems where nonlinear interactions and dissipation intertwine in complex ways. Here, we investigate a minimal model of a driven-dissipative quantum reservoir described by two coupled Kerr-nonlinear oscillators, an experimentally realizable platform that features controllable coupling, intrinsic nonlinearity, and tunable photon loss. Using Partial Information Decomposition (PID), we examine how different dynamical regimes encode input drive signals in terms of redundancy (information shared by each oscillator) and synergy (information accessible only through their joint observation). Our key results show that, near a critical point marking a dynamical bifurcation, the system transitions from predominantly redundant to synergistic encoding. We further demonstrate that synergy amplifies short-term responsiveness, thereby enhancing immediate memory retention, whereas strong dissipation leads to more redundant encoding that supports long-term memory retention. These findings elucidate how the interplay of instability and dissipation shapes information processing in small quantum systems, providing a fine-grained, information-theoretic perspective for analyzing and designing QRC platforms. Full article

In order to address the impact of the uncertainty and intermittency of a photovoltaic power generation system on the smooth operation of the power system, a microgrid scheduling model incorporating photovoltaic power generation forecast is proposed in this paper. Firstly, the factors affecting the accuracy of photovoltaic power generation prediction are analyzed by classifying the photovoltaic power generation data using cluster analysis, analyzing its important features using Pearson correlation coefficients, and downscaling the high-dimensional data using PCA. And based on the theories of the sparrow search algorithm, convolutional neural network, and bidirectional long- and short-term memory network, a combined SSA-CNN-BiLSTM prediction model is established, and the attention mechanism is used to improve the prediction accuracy. Secondly, a multi-temporal dispatch optimization model of the microgrid power system, which aims at the economic optimization of the system operation cost and the minimization of the environmental cost, is constructed based on the prediction results. Further, differential evolution is introduced into the QPSO algorithm and the model is solved using this improved quantum particle swarm optimization algorithm. Finally, the feasibility of the photovoltaic power generation forecasting model and the microgrid power system dispatch optimization model, as well as the validity of the solution algorithms, are verified through real case simulation experiments. The results show that the model in this paper has high prediction accuracy. In terms of scheduling strategy, the generation method with the lowest cost is selected to obtain an effective way to interact with the main grid and realize the stable and economically optimized scheduling of the microgrid system. Full article

We introduce the concept of ergodicity and explore its deviation caused by quantum scars in an isolated quantum system, employing a pedagogical approach based on a toy model. Quantum scars, originally identified as traces of classically unstable orbits in certain wavefunctions of chaotic systems, have recently regained interest for their role in non-ergodic dynamics, as they retain memory of their initial states. We elucidate these features of quantum scars within the same framework of this toy model. The integrable part of the model consists of two large spins, with a classical counterpart, which we combine with a random matrix to induce ergodic behavior. Scarred states can be selectively generated from the integrable spin Hamiltonian by protecting them from the ergodic states using a projector method. Deformed projectors mimic the ‘quantum leakage’ of scarred states, enabling tunable mixing with ergodic states and thereby controlling the degree of scarring. In this simple model, we investigate various properties of quantum scarring and shed light on different aspects of many-body quantum scars observed in more complex quantum systems. Notably, the underlying classicality can be revealed through the entanglement spectrum and the dynamics of ‘out-of-time-ordered correlators’. Full article

We implement a set of simulation experiments in NetSquid specifically designed to estimate the end-to-end fidelity across a path of quantum repeaters or quantum switches. The switch model includes several generalizations that are not currently available in other tools and are useful for gaining insight into practical and realistic quantum network engineering problems: an arbitrary number of memory registers at the switches, simplicity in including entanglement distillation mechanisms, arbitrary switching topologies, and routing protocols. An illustrative case study is presented, namely a comparison in terms of performance between a repeater chain where repeaters can only swap sequentially and a single switch equipped with multiple memory registers that is able to handle multiple swapping requests. Full article