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Quantum Zeitgeist Superposition

@superposition.bsky.social

Quantum Computing and Quantum Technology.

252 Followers  |  828 Following  |  3,597 Posts  |  Joined: 30.12.2023  |  2.0803

Latest posts by superposition.bsky.social on Bluesky

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Gaussian Approximation Accurately Simulates Gottesman-Kitaev-Preskill State Preparation With Loss The creation of robust continuous-variable quantum computing relies on generating high-quality Gottesman-Kitaev-Preskill (GKP) states, and a leading approach involves a β€˜cat breeding’ protocol using squeezed light and beam splitters. Researchers have now developed a new simulation technique to analyse how reliably this protocol functions when faced with inevitable optical loss, a significant challenge in quantum systems. By representing quantum states as combinations of simpler Gaussian shapes, the team accurately modelled multiple rounds of the breeding process, overcoming the computational difficulties of traditional methods. The results demonstrate that optical loss reduces the success rate of GKP state preparation, and critically, prevents the creation of fault-tolerant states when loss exceeds just four percent, highlighting a key limitation and target for improvement in quantum technologies. This simulation methodology is freely available as open-source code, enabling further research in the field
11.08.2025 20:32 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Autonomous Dynamics Suppress Non-Adiabatic Transitions In Landau-Zener Systems Adiabatic computing relies on carefully controlled processes, but unwanted transitions can slow down calculations, hindering its potential. Researchers now demonstrate a method to significantly accelerate these computations by suppressing these disruptive events, using a technique known as shortcuts to adiabaticity. The team focused on the Landau-Zener model, a fundamental system for understanding adiabatic dynamics, and introduced a clever approach involving coupling this system to a second, carefully tuned system. By adjusting the strength of this coupling, they successfully reduced the probability of unwanted transitions by over two orders of magnitude, effectively streamlining the computational process and showcasing the power of precisely designed control fields. This represents a key advance in maintaining the integrity and speed of adiabatic computations
11.08.2025 20:23 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Twisting Curved Superalgebras Unify Techniques In Supersymmetric Field Theory Supersymmetric field theory relies on techniques like twisting and the introduction of classical backgrounds, methods crucial for understanding phenomena from the Higgs mechanism to more complex calculations. Researchers now demonstrate a fundamental connection between these previously separate approaches, revealing both as instances of twisting curved superalgebras. This work establishes a unified algebraic framework utilising homotopy algebras and the Batalin-Vilkovisky formalism, successfully encompassing concepts such as holomorphic twists, spontaneous symmetry breaking, the treatment of anomalies, and supersymmetric localisation. Consequently, the team introduces a new definition of twisting applicable to algebras and presents a homotopy-algebraic reformulation of the one-particle-irreducible effective action, offering a more cohesive and powerful approach to these calculations
11.08.2025 20:18 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Yale Researchers Tame Coherent Noise With Teleportation Quantum computers face a significant challenge from coherent errors, which, unlike more common Pauli errors, can amplify over time and severely degrade performance, and currently lack established error correction thresholds. Researchers have now demonstrated that quantum teleportation offers a solution by effectively converting these complex coherent errors into simpler Pauli errors, for which robust correction strategies already exist. The team showed that repeatedly teleporting a single qubit actually suppresses errors, with the rate of increase comparable to that of Pauli errors, and crucially, they discovered that a specific model of coherent noise, over-rotation errors, becomes equivalent to a Pauli error model when applied to teleported quantum codes. This finding means that the performance of these codes, implemented using teleportation-based error correction, can be accurately simulated on conventional computers and possesses a provable error threshold, potentially eliminating the need for complex error mitigation techniques in future quantum systems
11.08.2025 20:12 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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VectorCDC Accelerates Hashless Data Deduplication Throughput By Up To 26.2x Data deduplication, a technique for minimising storage space, relies heavily on algorithms that identify and eliminate redundant data, but these algorithms often create performance bottlenecks. Researchers have developed VectorCDC, a new method to speed up these crucial processes by leveraging vector CPU instructions commonly found in modern processors like those from Intel, ARM, and IBM. The technique accelerates β€˜hashless’ content-defined chunking, which scans files to identify repeating data segments, without requiring computationally expensive hashing. Evaluation demonstrates VectorCDC significantly improves performance, achieving throughput increases of 8. 35 to 26. 2 times compared with existing vector-accelerated approaches, all while maintaining the same level of storage space savings. This innovation promises to substantially improve the efficiency of data storage systems
11.08.2025 20:08 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Thermal Annealing Restores Optical Properties In Crystal Ion Sliced Barium Titanate Flakes Barium titanate holds considerable promise for building compact photonic circuits, owing to its strong interaction with light, but manufacturing high-quality thin films has proven challenging. Researchers addressed this issue by refining a technique called crystal ion slicing, which creates thin barium titanate flakes, and then developed a post-processing thermal treatment to repair damage introduced during fabrication. Raman spectroscopy confirmed that this thermal step effectively restores the material’s crystalline structure, while second-harmonic generation microscopy revealed a reorganisation of the material’s ferroelectric domains, crucial for its optical properties. Importantly, the team found that strong optical signals persisted even in areas with some remaining structural imperfections, demonstrating a surprising resilience of the material’s key properties, and optical measurements confirmed the flakes behave similarly to bulk barium titanate. This combined approach establishes a scalable route to creating high-quality barium titanate-on-insulator platforms, paving the way for advanced photonic devices
11.08.2025 20:07 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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USF Concludes Inaugural AI & Robotics Research Programme. USF’s new Bellini College concluded a successful inaugural summer research program, bridging opportunity for undergraduate scholars. Professor Saundra Johnson Austin at the University of South Florida led the ten-week initiative, collaborating with the University of Rhode Island and funded by the Florida High Tech Corridor. This program addresses the disparity in research access between institutions, fostering connections and practical experience in robotics, cybersecurity, and AI for three undergraduate students. With the official launch of the Bellini College anticipated in autumn 2025, how will this model of inter-institutional collaboration shape the future of STEM research.
11.08.2025 17:36 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Set-Transformer Architecture Accelerates Variational Monte Carlo Calculations Of Magnetization Powers Recent advances in machine learning techniques are significantly enhancing the accuracy of variational Monte Carlo calculations, a powerful method for studying complex spin systems. Researchers now demonstrate how the set-transformer architecture, originally developed for natural language processing, can overcome a major limitation of this approach, the time-consuming calculation of observable properties. This innovative method bypasses traditional calculations by learning to predict these properties directly from random data, dramatically accelerating simulations, even for systems with long-range interactions. The team successfully applied this technique to both predict observable values and identify phase transitions, and importantly, they showed that knowledge gained from studying simpler systems can be transferred to more complex ones, reducing the overall computational cost and expanding the scope of these simulations
11.08.2025 17:29 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Trainability And Expressivity Linked By Initial State In Pulse-Based Quantum Machine Learning Pulse-based quantum machine learning represents a promising new approach to artificial intelligence, offering significant advantages in hardware efficiency. Researchers are now focusing on ensuring these models are not only efficient to train, but also capable of complex computations, a challenge previously hampered by issues of controllability and expressivity. This study investigates the crucial balance between these two factors, revealing a necessary condition linking the initial state of the quantum model, the method of measurement, and the underlying symmetries governing its dynamics. Through numerical simulations, the team demonstrates that adhering to this condition allows for the design of pulse-based models that avoid problematic training landscapes, while simultaneously maintaining the capacity to perform complex tasks, paving the way for practical applications of this emerging technology
11.08.2025 17:27 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Classical Solver Benchmarks Performance For Nuclear Magnetic Resonance Simulation Problems Simulating nuclear magnetic resonance (NMR) spectroscopy experiments presents a significant computational challenge, prompting researchers to explore whether quantum computing offers a practical advantage over conventional methods. The team developed and rigorously tested a classical solver specifically designed to address these complex simulations, pushing its capabilities beyond typical experimental parameters. Results demonstrate the solver performs effectively across a range of scenarios, although limitations emerge when simulating molecules with particularly unusual characteristics. This detailed benchmarking provides crucial insight into the specific hurdles that quantum algorithms must overcome to definitively demonstrate an advantage in the field of NMR spectroscopy, and highlights areas for future development
11.08.2025 17:25 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Virginia Tech Launches AI-Powered Shark Monitoring Database. One-third of shark species face extinction, yet crucial habitat and population data remains elusive. Professor Francesco Ferretti at Virginia Tech led the development of sharkPulse, an AI-powered platform scanning online sources for shark photos to automatically extract location and species data. This innovative approach addresses the critical data gap hindering shark conservation, enabling researchers to map populations and track changes with unprecedented scale and speed. 7 million images already processed, could this β€˜intelligent autonomous discovery’ revolutionise marine conservation efforts globally.
11.08.2025 17:24 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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UVic Study Maps Global Glacier Erosion Rates With Machine Learning. New research quantifies glacial erosion rates across nearly 180,000 glaciers globally, revealing the scale of landscape reshaping. Professor Sophie Norris at the University of Victoria led the study, utilising machine learning to estimate erosion rates for 85 per cent of modern glaciers. This analysis addresses the challenge of measuring glacial erosion in remote locations and indicates that 99 per cent of glaciers erode at between 0.68 millimeters per year. With glaciers historically sculpting landscapes, what implications will these erosion rates have for future geomorphological modelling and understanding of sediment transport.
11.08.2025 17:23 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Machine Learning Predicts Elastic Properties Of Lightweight Aluminum-Magnesium-Zirconium Alloys The pursuit of improved aluminium alloys drives innovation in energy-efficient engineering, and researchers are now leveraging machine learning to accelerate materials discovery. This study investigates the elastic properties of aluminium-magnesium-zirconium alloys, employing machine learning interatomic potentials (MLIPs) to predict how these materials respond to stress. The team developed two distinct MLIP approaches, one using Bayesian linear regression and another utilising an advanced neural network architecture, both trained using data from quantum mechanical simulations. Results demonstrate a strong correlation between these computationally efficient predictions and experimental ultrasonic measurements, with discrepancies remaining within a few GPa across all alloy compositions tested. This reliable predictive capability enables systematic exploration of alloy compositions, paving the way for the design of new aluminium alloys with specifically tailored physical and mechanical properties
11.08.2025 17:21 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Classical Surrogates Enable Scalable Inference For Quantum Machine Learning Models Quantum machine learning holds promise for industrial applications, but a lack of access to quantum hardware currently limits its widespread use. Researchers addressed this challenge by developing classical surrogates, essentially lightweight copies of quantum models, which allow computations to be performed on standard computers. Previous attempts to create these surrogates demanded substantial computing power, even for relatively small quantum models, hindering practical implementation. This new method significantly reduces computational demands by minimising redundancies in the process, achieving a scalable approach that requires far fewer resources. Testing on a real-world energy demand forecasting problem demonstrates high accuracy alongside a linear, rather than exponential, increase in computational needs, paving the way for faster integration of quantum-inspired solutions in industrial settings and providing a valuable tool for further research
11.08.2025 17:20 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Renormalized Stress-Energy Tensor Reveals Instabilities In Horizonless Bardeen Spacetime Vacuum Fluctuations Researchers investigate the behaviour of quantum fields in the curved spacetime surrounding a horizonless object, a theoretical alternative to black holes. They calculate the energy density created by vacuum fluctuations, revealing substantial differences between fields that interact with gravity in different ways, particularly at shorter distances. The team’s calculations demonstrate that, under certain conditions, these fluctuations can become unstable, growing exponentially and potentially disrupting the vacuum itself. Importantly, the study extends beyond simplified approximations, uncovering instabilities not predicted by earlier work and offering new insights into the fundamental nature of quantum fields in strong gravitational environments. This work challenges existing assumptions about vacuum stability and provides a crucial test for theoretical models describing the interaction between quantum mechanics and gravity
11.08.2025 17:18 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Reservoir Generative Adversarial Network Improves Accuracy Of Reservoir Computing Systems Reservoir computing represents a potentially energy-efficient alternative to traditional neural networks, offering advantages in processing large datasets, but current systems often lack the accuracy needed for real-world applications. Researchers are now exploring software-based improvements to overcome these limitations, and have developed a novel approach called Reservoir Generative Adversarial Network, or Reservoir GAN. This method leverages the strengths of reservoir computing by using it as the core β€˜generator’ within a generative adversarial network, a framework commonly used to create realistic data. Testing the system on handwritten digits and image datasets demonstrates that Reservoir GAN surpasses the performance of standard generative adversarial networks, conventional neural networks, and typical reservoir computers, suggesting a pathway to more accurate and efficient machine learning.
11.08.2025 17:16 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Chirality-Induced Spin Selectivity Arises From Helical Geometry, Study Confirms The phenomenon of chirality-induced spin selectivity (CISS) demonstrates that electrons travelling through chiral materials become spin-polarized at normal temperatures, yet the underlying mechanisms remain unclear. Researchers are investigating whether this effect arises from the helical shape of molecules or from chirality in a more general sense, seeking a unified explanation applicable across diverse systems, from solid films to individual molecules. This review clarifies the fundamental components of CISS, the nature of chirality, the induction of spin polarization, and the resulting spin selectivity, and surveys existing theoretical models. By examining applications such as spin-correlated radical pairs, which offer a flexible and biologically inspired platform, the work highlights the potential of CISS for emerging technologies in areas like spintronics and molecular sensing, ultimately aiming to bridge the gap between theoretical understanding and practical implementation
11.08.2025 17:08 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Kernel State Estimation Reconstructs Quantum States From Noisy Data Without Prior Knowledge Continuous-variable systems underpin numerous technologies, yet accurately representing their states presents a significant challenge. Researchers have now developed a new technique, kernel state estimation (KQSE), to reconstruct these states from noisy data without needing prior knowledge of the system. This method cleverly utilises classical statistical tools to associate states with probability distributions called tomograms, offering a practical alternative to complex mathematical representations. The results demonstrate that KQSE achieves a remarkably efficient convergence rate when estimating key properties like purity and overlap, and importantly, it performs well even with complex, non-Gaussian states, making it a valuable tool for advanced scientific applications. This robust approach allows for the characterisation of states in multiple bases, offering a comprehensive understanding of the system’s characteristics
11.08.2025 17:04 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Symmetry-Based Quantum Identity Authentication Schemes Enhance Secure Communication Protocols Quantum key distribution promises unconditionally secure communication, but its vulnerability lies in authenticating the identities of communicating parties. Researchers addressed this challenge by systematically reviewing three decades of quantum identity authentication protocols, identifying inherent symmetries to design new, more robust schemes. The team developed protocols utilising controlled secure direct communication, enabling mutual authentication between users with the help of a third party and employing Bell states to achieve this. Security analysis confirms the new protocols resist common attacks like impersonation and fraudulent authentication, while further work introduces quantum key distribution protocols that bypass the need for complex and expensive entangled photons or ideal single-photon sources, making them practical for implementation with currently available technology. Importantly, establishing key rate bounds demonstrates that careful classical data processing significantly improves the system’s tolerance to errors during transmission
11.08.2025 17:00 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Photonic Walk Demonstrates Bulk-Boundary Correspondence In Floquet Non-Abelian Topological Insulators Researchers now experimentally demonstrate the existence of Floquet non-Abelian topological insulators, a newly predicted state of matter arising in systems subjected to periodic, or β€˜driven’, forces. These materials exhibit unusual properties not found in conventional insulators, stemming from their unique topological charges which govern the behaviour of electrons within them. The team achieves this breakthrough by constructing a higher-dimensional photonic walk, essentially creating a light-based analogue of an electronic material, and employing innovative dynamic measurement techniques. Crucially, they combine direct observation of the underlying topological charge with spatially-resolved spectroscopy to confirm a key prediction of these materials: the appearance of conducting edge states even when the overall material appears non-conducting, establishing a definitive link between the material’s internal properties and its external behaviour.
11.08.2025 16:54 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Superconducting SQUID Response Characterised To Neutron And Gamma Radiation Sources Superconducting devices offer significant potential across diverse fields, from medical imaging to advanced computing, but their sensitivity to external disturbances presents a major challenge. Researchers investigated how these devices, specifically SQUIDs, respond to different types of radiation, exposing them to beams of neutrons and gamma rays. The experiments reveal that SQUIDs are notably sensitive to neutron fields, while remaining largely unaffected by gamma rays at the energy levels tested, demonstrating a clear distinction in their response. Analysis of the SQUID’s reaction to neutrons identifies two distinct types of signal disruptions, long-lasting bursts and short-lived peaks, which could potentially be used to characterise and classify faults within the device. To understand these differing responses, the team employed computer simulations, highlighting variations in how energy deposits and propagates within the SQUID when exposed to each type of radiation
11.08.2025 16:52 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Low-Weight Pauli Propagation Improves Variational Quantum Algorithm Parameter Initialisation Variational quantum algorithms frequently encounter challenges during optimisation, prompting researchers to explore whether classical techniques can improve performance. This study investigates the low-weight Pauli propagation algorithm as a potential classical aid to refine these quantum circuits, initially finding it to be an inaccurate estimator of the true energy. However, the research reveals a surprising benefit: despite its numerical limitations, the algorithm effectively maps out a reliable optimisation landscape, guiding parameters towards promising solutions. Consequently, the team proposes using low-weight Pauli propagation not as a direct optimisation method, but as a pre-processor to identify superior starting parameters for the main variational quantum algorithm, demonstrably enhancing both accuracy and convergence rates by up to tenfold when tested on complex Heisenberg models. This reframes the algorithm as a valuable classical tool, reducing the computational demands on emerging quantum hardware and mitigating the optimisation hurdles inherent in variational quantum computation
11.08.2025 16:51 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Cross-Rotation Scheme Enables High-Dimensional Reconciliation In Continuous-Variable QKD Systems Multidimensional rotation improves information reconciliation in continuous-variable quantum key distribution, a technique for secure communication, but past limitations restricted its effectiveness to lower dimensions. Researchers now present a cross-rotation scheme that overcomes these restrictions, enabling reconciliation in arbitrarily high dimensions, provided they are even multiples of eight. The method involves reshaping data into a matrix and applying orthogonal transformations to its columns and rows, effectively increasing the reconciliation dimension with each rotation while minimising the amount of data sent over conventional channels. Performance analysis reveals that 64-dimensional cross-rotation nearly reaches the theoretical limit, suggesting it represents a practical and highly efficient approach for extending the range and security of quantum communication systems
11.08.2025 16:49 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Gate Reflectometry Distinguishes Key Processes For Forming Kitaev Chains In Quantum Dots Researchers are exploring hybrid quantum dot (QD) and superconductor systems as a pathway to creating stable quantum bits, known as Majorana qubits, which promise greater resilience to environmental noise. This work demonstrates a powerful technique, radio-frequency gate reflectometry, to rapidly and non-invasively probe these systems and understand the crucial interactions between quantum dots. By carefully analysing the reflected signal, the team successfully distinguishes between two key processes, elastic cotunneling and crossed-Andreev reflection, that are essential for building a functional quantum chain. Importantly, the measurements reveal clear evidence of parity switching, a change in the quantum state between even and odd possibilities, even when the system is isolated, confirming that gate reflectometry effectively captures the underlying physics of these promising quantum systems
11.08.2025 16:47 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Diamond Optomechanical Crystals Achieve Long Coherence Times For Spin-Based Quantum Technologies Diamond optomechanical crystals represent a promising avenue for advances in networking and computing, as they bridge the gap between mechanical vibrations, light, and the quantum properties of individual spins. Researchers have now created these tiny structures with exceptionally high-quality mechanical vibrations, maintaining the delicate quantum coherence of embedded nitrogen-vacancy (NV) centers within the diamond, a crucial step previously difficult to achieve. The team employed a reliable method for creating single-crystal diamond membranes, then grew additional diamond layers using a chemical vapour deposition process, allowing for precise control over the material’s properties. This resulted in devices exhibiting coherence times exceeding 270 seconds for the NV centers, alongside a high optomechanical cooperativity, indicating strong interactions between light and mechanical motion, and paving the way for hybrid spin-mechanical devices operating in the quantum regime
11.08.2025 16:43 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Simulation And Optimisation Method Quantifies Error Sources In Entangled Photon Generation Generating high-quality quantum states represents a significant challenge in developing advanced quantum technologies, including computing and communication, as various errors degrade performance. Researchers have now developed an automated method to pinpoint specific error sources that diminish quantum state quality, moving beyond standard diagnostic tools which often combine multiple errors into a single measurement. The team focused on entangled photon pairs, building a simulation that models potential error sources with adjustable parameters, and then optimising these parameters to closely match experimental data. This process dramatically reduced the difference between the simulated and experimental states, explaining 86% of the observed errors and validating the accuracy of the modelled error sources. The modular design of this framework suggests it can be adapted to analyse and improve the performance of diverse quantum systems, including atoms and solid-state spins
11.08.2025 16:35 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Transmon Qubit Layouts Optimised Using Simulation And Design Iteration Creating practical quantum computers requires designing systems that maintain coherence and minimise environmental noise, a significant challenge for scalability. Researchers are increasingly focusing on superconducting circuits, particularly those based on the Transmon architecture, due to their potential for lithographic fabrication and reduced sensitivity to interference. This work presents an integrated framework combining circuit design with detailed material analysis to optimise performance, utilising Qiskit Metal for layout creation and Ansys HFSS to extract key parameters like eigenfrequencies and participation ratios. Through simulations in COMSOL Multiphysics, the team investigated how different materials impact energy loss and electromagnetic properties, demonstrating a clear link between material choice and device quality. This approach offers a viable pathway towards building reliable, scalable quantum systems and supports ongoing efforts to achieve fault-tolerant quantum computation
11.08.2025 16:33 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Entangled States On IBM Brisbane Demonstrate Quantum Communication And Model Simulation Quantum communication relies on the delicate properties of quantum systems, and this research explores how to establish and maintain these properties using the IBM Brisbane quantum processor. The team simulates and controls quantum systems, focusing on fundamental operations like implementing quantum gates and carefully analysing the behaviour of entangled states, which are crucial for secure information transfer. By incorporating realistic conditions such as noise and decoherence into their simulations, researchers assess the practical feasibility of using entangled states for quantum networks. The findings demonstrate the potential of these systems to advance quantum information science, offering insights into complex interactions and paving the way for both foundational research and real-world applications in secure communication technologies
11.08.2025 16:32 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Johns Hopkins APL Demonstrates Quantum Speedup For Text Analysis. Johns Hopkins APL researchers demonstrate a quantum algorithm accelerating semantic text analysis, outperforming classical methods. Professor David Williams’ team at the Applied Physics Laboratory achieved a speedup using quantum random walks on complex text datasets. This breakthrough addresses the challenge of analysing vast online information volumes and could enable faster identification of emerging threats and narratives. With potential for multilingual applications, could quantum semantic analysis provide more nuanced insights than current AI-driven approaches.
08.08.2025 09:57 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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University Of Missouri Tracks Invasive Trees With AI Satellite Imagery. University of Missouri researchers develop a low-cost method to track invasive Callery pear trees using satellite imagery. Justin Krohn at the University of Missouri trained a machine learning model to identify these trees based on light reflection, offering an affordable alternative to drone surveys. This research addresses the challenge of invasive species management and could enable more effective conservation strategies for Missouri’s ecosystems, particularly within the Mark Twain National Forest. As suburban areas expand, will this technology prove crucial in predicting and mitigating the spread of other invasive plant species across the Midwest.
08.08.2025 09:53 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0

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