Search Results (260)

Quantum walks (QWs) represent pillars of quantum dynamics and information processing. They provide a powerful framework for simulating quantum transport, designing search algorithms, and enabling universal quantum computation. Several physical platforms have been employed for their implementation, such as trapped atoms and ions, nuclear magnetic resonance systems, and photonic quantum architectures either in bulk optics or waveguide structures and fiber loop networks. Here we focus on the most promising and versatile approach, which is photonic integrated circuits. In this work, we review how the employment of this versatile experimental platform has allowed exploring several phenomena related to QW-based protocols, such as evolution in the presence of different kinds of noise. In this landscape, to the best of our knowledge, few examples report on the introduction of absorbing centers and their effects on the coherence of the dynamics. Here we present and discuss the results related to the absorbing boundaries in QWs, obtained through theoretical simulations and experiments conducted with the universal photonic quantum processors realized by QuiX Quantum. We analyze how localized absorption along one lattice edge affects the walker dynamics, depending on both the leakage probability and the initial injection site. Our results suggest that the presence of controlled losses modifies interference patterns and coherence without fully destroying quantum features and providing an effective resource for engineering on-chip QWs and simulating open quantum systems. Full article

The automation of petroleum extraction columns requires robust and adaptive control due to the highly nonlinear nature of the heat and mass transfer processes involved. In this study, a hybrid control system integrating conventional fuzzy logic with quantum-inspired computational optimization is proposed to enhance the control of temperature and flow rates in industrial extraction columns. The hybrid quantum-inspired fuzzy controller is applied to a petroleum extraction column. The controller adopts fuzzy rule weights using a quantum-inspired optimization algorithm. Compared with classical PID and fuzzy controllers, it reduces settling time and solvent consumption. A MATLAB/Simulink-based simulation model of the extraction column was developed to validate the approach. Experimental tests were conducted using synthetic data and varying operational parameters to evaluate control performance. The hybrid controller achieved a 0.7% reduction in phenol consumption and reduced temperature deviations by 2.2% compared to a baseline fuzzy controller. Energy savings ranged from 1% to 2% depending on the operating scenarios. These results were confirmed through repeated simulations and statistical analysis. The proposed system demonstrates the potential of quantum-inspired fuzzy control to enhance process efficiency, reduce energy use, and improve product quality in complex chemical extraction applications. The statistical evaluation was based on repeated simulation runs and comparative performance metrics rather than physical experiments. Full article

We present a theoretical framework integrating quantum optimization with DNA-based molecular storage for enhanced image compression, validated via classical simulation in IBM Qiskit. The proposed Quantum-DNA Image Compression (Q-DIC) framework formulates DNA codon selection as a quantum search problem, applying Grover’s algorithm to achieve $\mathrm{O}\left(\sqrt{N}\right)$ speedup in exploring the 4⁸ = 65,536-codon solution space. Key contributions include (1) novel multi-objective cost functions balancing reconstruction fidelity, thermodynamic stability, and synthesis feasibility; (2) quantum-inspired stabilizer codes achieving 10⁸-fold error suppression with 23% overhead reduction versus Reed–Solomon codes; (3) NISQ-compatible implementation achieving 12.3× compression on current quantum hardware. Simulation experiments across diverse image categories demonstrate 8.9× realistic compression ratio (18.3× theoretical maximum). Hardware validation on IBM Quantum systems achieved 10.8–11.2× compression, confirming practical viability. Critical assessment identifies implementation gaps: current hardware supports hundreds of gates versus the required amount of 60,000–800,000, and DNA synthesis costs require 1000× reduction for economic viability. Despite being simulation-based, this work establishes rigorous foundations for quantum–molecular hybrid architectures and provides a validated pathway for experimental confirmation. Full article

This paper identifies theoretical vulnerabilities in quantum integrity verification by demonstrating that Bell inequality (BI) violations, central to the detection of quantum entanglement, can align with predictions from hidden variable theories (HVTs) under specific measurement configurations. By invoking a Heisenberg-inspired measurement resolution constraint and finite-resolution positive operator-valued measures (POVMs), we identify “convergence vicinities” where the statistical outputs of quantum and classical models become operationally indistinguishable. These results do not challenge Bell’s theorem itself; rather, they expose a vulnerability in quantum integrity frameworks that treat observed Bell violations as definitive, experiment-level evidence of nonclassical entanglement correlations. We support our theoretical analysis with simulations and experimental results from IBM quantum hardware. Our findings call for more robust quantum-verification frameworks, with direct implications for the security of quantum computing, quantum-network architectures, and device-independent cryptographic protocols (e.g., device-independent quantum key distribution (DIQKD)). Full article

Polarization beam combining (PBC) is an important technology for enhancing laser brightness. The conventional bulk polarization beam combiners are Brewster plates and birefringent polarization prisms. However, in the mid- and long-wave infrared range, the beam combining performance is limited by the transmission and birefringent coefficient of the available materials. In this paper, a polarization beam combiner based on a reflection metasurface was proposed. The phases of incident beams with orthogonal linear polarizations were individually manipulated by the side lengths of the rectangular silicon pillar. A metasurface polarization beam combiner operating band was designed and fabricated. When the two beams at 4.6 μm with orthogonal linear polarizations were incident on the metasurface at angles of −13.3° and 13.3°, respectively, they were reflected in the 0°-direction. The overall beam combining efficiency was 88.9%. When both of the quantum cascade lasers used in the experiments were in the fundamental transverse Gaussian mode, the measured beam quality factors M² of the combined beam were 1.21 and 1.14 along the fast and slow axes, respectively. Both simulation and experimental results demonstrated that the proposed metasurface was an efficient polarization beam combiner with negligible wavefront distortion. It is a promising alternative to traditional bulk optics for the mid- and long-wave infrared. Full article

Iris recognition continues to pose challenges for deep learning models, despite its status as one of the most reliable biometric authentication techniques. These challenges become more pronounced when training data is limited, as subtle, high-dimensional patterns are easily missed. To address this issue and strengthen both feature extraction and recognition accuracy, this study introduces IRIS-QResNet, a customized ResNet-18 architecture augmented with a quanvolutional layer. The quanvolutional layer simulates quantum effects such as entanglement and superposition and incorporates sinusoidal feature encoding, enabling more discriminative multilayer representations. To evaluate the model, we conducted 14 experiments on the CASIA-Thousands, IITD, MMU, and UBIris datasets, comparing the performance of the proposed IRIS-QResNet with that of the IResNet baseline. While IResNet occasionally yielded subpar accuracy, ranging from 50.00% to 98.66%, and higher loss values ranging from 0.1060 to 2.0640, comparative analyses showed that IRIS-QResNet consistently outperformed it. IRIS-QResNet achieved lower loss (ranging from 0.0570 to 1.8130), higher accuracy (ranging from 66.67% to 99.55%), and demon-started improvement margins spanning from 0.1870% in the CASIA End-to-End subject recognition with eye-side to 16.67% in the MMU End-to-End subject recognition with eye-side. Loss reductions ranged from 0.0360 in the CASIA End-to-End subject recognition without eye-side to 1.0280 in the UBIris Non-End-to-End subject recognition. Overall, the model exhibited robust generalization across recognition tasks despite the absence of data augmentation. These findings indicate that quantum-inspired modifications provide a practical and scalable approach for enhancing the discriminative capacity of residual networks, offering a promising bridge between classical deep learning and emerging quantum machine learning paradigms. Full article

Water pollution from textile effluents poses serious environmental risks, particularly due to persistent anionic dyes such as Reactive Black 5 (RB5). This study demonstrates that simple deposition of Y₂O₃ onto commercially available, biobased TiO₂ (bTiO₂) significantly enhances photocatalytic degradation efficiency under simulated sunlight, suppressing rapid recombination of electron–hole pairs. Addressing a key research gap, the proposed method replaces expensive nanoscale precursors and complex synthesis routes typically used for Y₂O₃/TiO₂ systems with a low-cost, straightforward approach involving weak complexation and co-precipitation. The resulting Y₂O₃-bTiO₂ composite was characterized using FTIR, XRD, SEM, EDX, TEM, XPS, and UV-DRS techniques, confirming efficient incorporation of Y₂O₃ on the TiO₂ surface. Photocatalytic experiments revealed that nanoparticles calcined at 700 °C achieved complete RB5 degradation within 60 min—reducing the reaction time by half compared to undoped bTiO₂. Systematic studies of initial dye concentration, catalyst loading, and irradiation time confirmed that the degradation followed pseudo-first-order kinetics with a rate constant of 0.064 min⁻¹ (R² = 0.98). Calculated quantum yields corroborated the reduced electron–hole recombination induced by Y₂O₃ deposition. These findings highlight the novelty and practicality of the developed Y₂O₃-bTiO₂ photocatalyst as an efficient, affordable, and environmentally sustainable material for the degradation of industrial dyes. Full article

This paper presents a voltage-controlled phase shifter (VCPS) capable of 360° continuous adjustment, applied in laser frequency-locking systems to obtain maximum amplitude error signals with minimal dispersion. The phase-shifting unit is realized through CMOS integrated circuit design, utilizing comparators, logic gate control modules, and filters. Simulations verify the VCPS, composed of three cascaded units, achieves 360° continuous phase adjustment. A printed circuit board (PCB) was fabricated with the integration of electronic components. The test results demonstrate that the VCPS exhibits a continuous 360° phase shift in one direction with increasing control voltage. It operates from kHz to 50 MHz and maintains a peak-to-peak output amplitude of 5 V or 10 V. The proposed VCPS has been successfully applied in cold-atom interferometry, quantum memory experiments, and optical frequency standards. Full article

Real-time quality monitoring during oyster cold chain transportation is a critical component in ensuring food safety. Addressing the issues of high redundancy and insufficient environmental adaptability in existing electronic nose systems, this study proposes a multi-algorithm collaborative optimization strategy for sensor array optimization. The system integrates ten gas sensors (TGS series, MQ series), employing Random Forest (RFA), Simulated Annealing (SA), and Genetic Quantum Particle Swarm Optimization (GA-QPSO) for sensor selection. KNN combined with K-means analysis validates the optimization outcomes. Under cold chain environments at 4 °C, 12 °C, 20 °C, and 28 °C, a multidimensional dataset was constructed by extracting global variables using feature correlation functions. Experiments demonstrate that the optimized sensor count decreases from 10 to 5–6 units while maintaining recognition accuracy above 95%, with redundancy decreased by over 40%. This multi-algorithm collaborative optimization effectively balances sensor array recognition precision, resource efficiency, and environmental adaptability, providing an intelligent, high-precision technical solution for oyster cold chain monitoring. Full article

This work aims to maximize the Hawking emission temperature arising in the optical analog model of the event horizon of an astrophysical black hole. A weak probe wave interacts with an intense ultrashort optical pulse via the Kerr effect in a photonic crystal fiber. This interaction causes the probe wave to experience an effective spacetime geometry characterized by the presence of an optical event horizon, where the analogous Hawking radiation effect arises. Here we refer to the simulated or classical version of the analog of Hawking radiation. This study considers four distinct types of photonic crystal fibers with anomalous dispersion curves that allow for maximizing the effect. Our first three numerical simulations indicate that a Hawking emission temperature of up to 361 K can be achieved with a photonic crystal fiber with two zero-dispersion wavelengths, while the emission temperature values in the original investigation are lower than 244 K. And in the fourth, we can see that we have a configuration in which the temperature can be improved up to 1027 K. Moreover, these results also emphasize the feasibility of using analog models to test the quantum effects of gravity, such as Hawking radiation produced by typical black holes, whose magnitude is far below the temperature of the cosmic microwave background (2.7 K). Full article

Quantum machine learning (QML) integrates quantum computing with classical machine learning. Within this domain, QML-CQ classification tasks, where classical data is processed by quantum circuits, have attracted particular interest for their potential to exploit high-dimensional feature maps, entanglement-enabled correlations, and non-classical priors. Yet, practical realizations remain constrained by the Noisy Intermediate-Scale Quantum (NISQ) era, where limited qubit counts, gate errors, and coherence losses necessitate frugal, noise-aware strategies. The Data Re-Uploading (DRU) algorithm has emerged as a strong NISQ-compatible candidate, offering universal classification capabilities with minimal qubit requirements. While DRU has been experimentally demonstrated on ion-trap, photonic, and superconducting platforms, no implementations exist for spin-based quantum processing units (QPU-SBs), despite their scalability potential via CMOS-compatible fabrication and recent demonstrations of multi-qubit processors. Here, we present a pulse-level, noise-aware DRU framework for spin-based QPUs, designed to bridge the gap between gate-level models and realistic spin-qubit execution. Our approach includes (i) compiling DRU circuits into hardware-proximate, time-domain controls derived from the Loss–DiVincenzo Hamiltonian, (ii) explicitly incorporating coherent and incoherent noise sources through pulse perturbations and Lindblad channels, (iii) enabling systematic noise-sensitivity studies across one-, two-, and four-spin configurations via continuous-time simulation, and (iv) developing a noise-aware training pipeline that benchmarks gate-level baselines against spin-level dynamics using information-theoretic loss functions. Numerical experiments show that our simulations reproduce gate-level dynamics with fidelities near unity while providing a richer error characterization under realistic noise. Moreover, divergence-based losses significantly enhance classification accuracy and robustness compared to fidelity-based metrics. Together, these results establish the proposed framework as a practical route for advancing DRU on spin-based platforms and motivate future work on error-attentive training and spin–quantum-dot noise modeling. Full article

The integration of autonomous robots with intelligent electrical systems introduces complex energy management challenges, particularly as microgrids increasingly incorporate renewable energy sources and storage devices in widely distributed environments. This study proposes a quantum-inspired multi-agent reinforcement learning (QI-MARL) framework for energy-aware swarm coordination in smart microgrids. Each robot functions as an intelligent agent capable of performing multiple tasks within dynamic domestic and industrial environments while optimizing energy utilization. The quantum-inspired mechanism enhances adaptability by enabling probabilistic decision-making, allowing both robots and microgrid nodes to self-organize based on task demands, battery states, and real-time energy availability. Comparative experiments across 1500 grid-based simulated environments demonstrated that when benchmarked against the classical MARL baseline, QI-MARL achieved an 8% improvement in path efficiency, a 12% increase in task success rate, and a 15% reduction in energy consumption. When compared with the rule-based approach, improvements reached 15%, 20%, and 26%, respectively. Ablation studies further confirmed the substantial contributions of the quantum-inspired exploration and energy-sharing mechanisms, while sensitivity and scalability analyses validated the system’s robustness across varying swarm sizes and environmental complexities. The proposed framework effectively integrates quantum-inspired AI, intelligent microgrid management, and autonomous robotics, offering a novel approach to energy coordination in cyber-physical systems. Potential applications include smart buildings, industrial campuses, and distributed renewable energy networks, where the system enables flexible, resilient, and energy-efficient robotic operations within modern electrical engineering contexts. Full article

Superconducting Transition Edge Sensors (TESs) are a promising technology for fundamental physics applications due to their low dark count rates, excellent energy resolution, and high detection efficiency. On the DESY campus, we have been developing a program to characterize cryogenic quantum sensors for fundamental physics applications, particularly focused on TESs. We currently have two fully equipped dilution refrigerators that enable simultaneous TES characterization and fundamental physics searches. In this paper, we summarize the current status of our TES characterization, including recent calibration efforts and efficiency measurements, as well as simulations to better understand TES behavior and its backgrounds. Additionally, we summarize some physics applications that we are already exploring or planning to explore. We will give preliminary projections on a direct dark matter search with our TES, where exploiting low-threshold electron scattering in superconducting materials allows us to search for sub-MeV-scale dark matter. We are also working toward performing a measurement of the even-number photon distribution (beyond one pair) of a quantum-squeezed light source. Finally, if it proves to meet the requirements, our TES detector may be used as a second, independent detection system to search for an axion signal at the ALPS II experiment. Full article

Quantum spin transport in integrable systems reveals a rich nonequilibrium phenomena that challenges the conventional hydrodynamic framework. Recent advances in ultracold atom experiments with state preparation and single-site addressing have enabled the understanding of this anomalous behavior. Particularly, the full universality characterization of exotic initial states, as well as their measurement representation, remain unknown. By employing tensor network and contrast methods, we systematically investigate spin transport in the quantum XXZ spin chain and extract dynamical scaling exponents emerging from two paradigmatic and experimentally attainable initial states, i.e., multi-periodic domain-wall (MPDW) and spin-helix (SH) states. Our results using different values of anisotropic parameters $\Delta \in [0,1.2]$ demonstrate the evident impeded transport and the difference between the two states with increasing $\Delta$ values. Large-scale and consistent simulations confirm the contrast method as a viable scaling extraction approach for exotic states with periodicity within experimentally accessible timescales. Our work establishes a foundation for studying initial memory and the corresponding relations of emergent transport behavior in nonequilibrium quantum systems, opening avenues for the identification of their unique universality classes. Full article

The simulation of multidimensional wave propagation with variable material parameters is a computationally intensive task, with applications from seismology to electromagnetics. While quantum computers offer a promising path forward, their algorithms are often analyzed in the abstract oracle model, which can mask the high gate-level complexity of implementing those oracles. We present a framework for constructing a quantum algorithm for the multidimensional wave equation with a variable speed profile. The core of our method is a decomposition of the system Hamiltonian into sets of mutually commuting Pauli strings, paired with a dedicated diagonalization procedure that uses Clifford gates to minimize simulation cost. Within this framework, we derive explicit bounds on the number of quantum gates required for Trotter–Suzuki-based simulation. Our analysis reveals significant computational savings for structured block-model speed profiles compared to general cases. Numerical experiments in three dimensions confirm the practical viability and performance of our approach. Beyond providing a concrete, gate-level algorithm for an important class of wave problems, the techniques introduced here for Hamiltonian decomposition and diagonalization enrich the general toolbox of quantum simulation. Full article