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Leeds researchers balance quantum repeater speed and reach
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Leeds researchers balance quantum repeater speed and reach

Javier Rey-Domínguez and Mohsen Razavi of the School of Electronic and Electrical Engineering at the University of Leeds have proposed a new approach to quantum repeaters, published August 21, 2026, in Quantum Science and Technology. The researchers address the challenge of long-distance quantum communication by prioritizing scalability, feasibility, and interoperability in their design. Their work details a solution using a hop-by-hop entanglement swapping approach and simple error detection, aiming to adapt to current Internet infrastructure without demanding overly complex physical devices. The paper reports how this method could enable trust-free continental quantum key distribution through staged development. Leeds Researchers Address Quantum Repeater Scalability A new quantum repeater design from the University of Leeds prioritizes practical implementation alongside distance, a departure from approaches focused solely on maximizing reach. This focus on scalability, feasibility, and interoperability aims to bridge the gap between theoretical quantum communication and real-world network deployment. The Leeds team’s design diverges from traditional quantum repeaters by adopting a sequential entanglement generation (SEG) approach, mirroring the packet-switched networks used in conventional internet infrastructure. Rather than reserving dedicated pathways for end-to-end entanglement, the system generates and swaps entanglement incrementally, utilizing available resources as they become free. This strategy, inspired by classical techniques, allows multiple users to share network resources without persistent allocation, potentially increasing efficiency and reducing coordination overhead. Crucially, the researchers addressed the challenge of error propagation without resorting to complex quantum error correction. Instead of actively correcting errors, their repeater design detects them, aborting a round of SEG upon identification. This simplification reduces hardware

Aug 23, 2026

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Researchers classify neutrino events with a quantum computerquantum-computing

Researchers classify neutrino events with a quantum computer

Researchers have achieved testing accuracy near 80% with the NPQK and approximately 70% accuracy with the QCNN in classifying events detected by neutrino telescopes using a quantum computer, a result comparable to traditional methods. Pablo Rodriguez-Grasa, University of the Basque Country UPV/EHU and colleagues demonstrated this capability by investigating neural projected quantum kernels and quantum convolutional neural networks. This work, published August 21, 2026, in Quantum Science and Technology, Number 4, establishes the feasibility of applying quantum machine learning to astronomical data analysis with current hardware. The study explores how quantum computers can distinguish between different types of neutrino events, crucial for understanding rare cosmic phenomena. NPQK and QCNN Approaches to Neutrino Event Classification Achieving testing accuracy near 80%, the neural projected quantum kernel (NPQK) approach demonstrated a capacity to classify neutrino events directly on both simulators and the IBM Strasbourg quantum processor. This result suggests a shift toward practical quantum applications in astrophysics. Researchers led by Pablo Rodriguez-Grasa at the University of the Basque Country UPV/EHU detailed this performance in a study published August 21, 2026, in Quantum Science and Technology, Number 4, focusing on distinguishing between muon tracks and hadronic/electromagnetic cascades, key signatures within neutrino telescope data. This direct implementation on quantum hardware bypasses the need for purely simulated results, validating the methodology against the inherent noise and limitations of current quantum systems. The team addressed a critical challenge in applying quantum machine learning to high-energy physics: the encoding of large feature spaces. Traditional methods struggle with the vast amounts of information generated by neutrino telescopes like IceCube, limiting the feasibility of quantum graph neural networks. To address this, Rodrigue

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What Does a D-Wave Quantum Insider's Sale of 23,850 Shares Mean for Investors?quantum-computing

What Does a D-Wave Quantum Insider's Sale of 23,850 Shares Mean for Investors?

Sophie C. Ames, Chief Human Resources Officer of D-Wave Quantum Inc. (QBTS +8.46%), disposed of 23,850 shares of common stock on August 17, 2026 according to a recent SEC Form 4 filing.Transaction summaryMetricValueTransaction value~$504,905Shares sold23,850Post-transaction shares (directly held)565,159Post-transaction value$11.79 millionTransaction value based on SEC Form 4 weighted average sale price ($21.17); post-transaction value based on August 17, 2026 market close ($20.87).Key questionsWhat prompted this disposition of shares?The disposal was a non-discretionary transaction executed to satisfy tax obligations upon the vesting of restricted stock units (RSUs) and does not reflect a change in the insider's investment outlook.What is the current status of the executive's equity position?Following this transaction, the executive maintains a direct position of 565,159 shares, which includes 536,144 unvested restricted stock units.How has the stock performed relative to this transaction?Shares were priced at $21.17 during this transaction, while the company has realized a 23% one-year return as of the August 17, 2026 market close.What is the company's current financial profile?D-Wave Quantum has a market cap of $7.7 billion and reported trailing twelve-month revenue of $12.4 million, with total insider ownership standing at 0.15%.Company OverviewMetricValueShare Price (as of market close 2026-08-17)$20.87Market Capitalization$7.7 billionRevenue (TTM)$12.4 millionNet Income (TTM)-$248.7 millionCompany SnapshotD-Wave Quantum Inc. develops and supplies quantum computing systems, software, and related services, including its flagship Advantage quantum computer, the Ocean open-source programming toolkit, and Leap, a cloud-based platform for real-time quantum computing access.The company generates revenue through licensing quantum computing systems, providing cloud-based quantum computing services via its Leap platform, offering professional services and consulting for

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