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India National Quantum Mission

Explore India's ₹6,003 Crore quantum initiative - 4 thematic hubs, leading startups, and latest developments

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Top 10 Quantum Computing Companies in France

France’s quantum ecosystem spans neutral atoms, cat qubits, photonics, silicon spin processors, quantum networking and sensing. QuantumNews ranks the ten companies leading this diverse and increasingly commercial technology landscape.

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Sunlight generates entangled photon pairs for the first time

Researchers have achieved this milestone despite sunlight's low coherence and intensity, offering a sustainable alternative to lasers.

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Gujarat to Launch State Quantum Mission, Joining India’s Race to Build Regional Quantum Hubs

The Gujarat government plans to establish a dedicated Quantum Mission to accelerate research, skills and practical applications across the state. The initiative will complement India’s ₹6,003.65-crore National Quantum Mission, although Gujarat is yet to announce its budget, governance structure or implementation timeline.

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Top 10 Quantum Computing Companies in Japan

Japan is quietly building one of the world's most complete quantum ecosystems — spanning superconducting processors, cold neutral-atom hardware, cryogenic materials, quantum software, and pharma-grade quantum chemistry. According to QuantumNews' Japan country page, the ecosystem now counts 52 companies, 8 university research clusters, and 1 national research institution, concentrated heavily around Tokyo (35 companies), with smaller hubs in Osaka, Wako, Yokohama, Chigasaki, Kyoto, Hamamatsu, and Tsukuba. Here's a deep dive into the Top 10 Japanese quantum computing companies, ranked by their QuantumNews Quantum Score — a composite measure of hardware scale, gate fidelity/error-correction maturity, funding raised & investor quality, and commercial momentum & partnerships.

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India’s 1,000-Km Quantum-Secure Network Marks a Defining Moment for the National Quantum Mission

India’s National Quantum Mission has demonstrated a 1,000-kilometre quantum-secured communication network using indigenous technology developed by QNu Labs. The milestone takes India halfway towards its 2,000-kilometre inter-city QKD target and strengthens the country’s position in quantum communication and cybersecurity.

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Silicon-Spin Quantum Computers Move Closer to Integrated Chips

Researchers have integrated silicon spin qubits, cryogenic CMOS control and superconducting interconnects into a working quantum processing unit. The advance addresses one of the biggest barriers to scaling silicon quantum computers: controlling large numbers of qubits without overwhelming cryogenic systems with wiring.

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Harvest Now, Decrypt Later: Why the Quantum Cybersecurity Threat Has Already Begun

A cryptographically powerful quantum computer does not yet exist, but sensitive encrypted information may already be under threat. The “harvest now, decrypt later” strategy allows adversaries to collect protected data today and wait for future quantum systems to break the encryption, making post-quantum migration an immediate cybersecurity priority.

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US National Quantum Update: July 2026

The United States advanced its quantum strategy in July through defence-backed benchmarking, fault-tolerance research, domestic manufacturing and post-quantum security, as federal policy and company activity moved closer together.

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IBM Says It Has Reached Verifiable Quantum Advantage. Here Is Why the Claim Matters

IBM says its latest quantum-computing experiments demonstrate a form of verifiable quantum advantage: calculations that challenge leading classical methods while still producing results that can be tested for reliability. This analysis explains what IBM has actually claimed, why verification is a major issue in quantum computing, how the technology reached this stage, and what the development could mean for India and the global technology industry.

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Freezing Swampland: How Krylov Complexity Reframes the Weak Gravity Conjecture

A new holographic study links the Weak Gravity Conjecture to the freezing and unfreezing of Krylov spread complexity in charged black-hole duals, offering a quantum-information perspective on a core swampland criterion.

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Canada’s Top 10 Quantum Computing Companies: The Companies Leading the Charge

Canada has built one of the world’s most significant and diverse quantum technology ecosystems.

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Quantum Computing Weekly: From Bigger Machines to Practical Quantum Value

Quantum computing is moving beyond the race for bigger processors. This week’s developments across neutral atoms, photonics, quantum software, industrial optimisation, cryogenic infrastructure and workforce development show an industry increasingly focused on practical value, system integration and real-world deployment.

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Unlocking India's Quantum Future: From Better Medicines to Smarter Infrastructure

Quantum computing is often described as the next technological revolution. But where can it realistically create value for India? From healthcare and clean energy to logistics and cybersecurity, the country's biggest opportunities may lie in solving real national challenges rather than chasing technological hype.

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Quantum Computing’s Next Breakthrough Isn’t Just Hardware, It’s Purpose

As quantum hardware advances, the industry’s next challenge is identifying applications where quantum systems can deliver sustained scientific, strategic and economic value.

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Top 10 Quantum Computing Companies in the United Kingdom — Ranked by QuantumNews

The United Kingdom has become one of the most interesting quantum ecosystems to watch because its companies are not all trying to solve the same problem. Some are building quantum processors. Some are working on photonic systems. Some are focused on trapped ions, superconducting qubits, silicon spin qubits, quantum networking, quantum-safe encryption, and quantum sensing. That diversity matters because the quantum industry will not be built by one technology path alone. It will be shaped by hardware breakthroughs, software infrastructure, secure communications, error correction, quantum networking, and industry-ready deployment models

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Top 10 Quantum Startups in India 2026: Driving India’s National Quantum Mission

From indigenous quantum computers and secure communication networks to photonic processors, quantum sensors and enterprise software, these startups are building the foundations of India’s emerging quantum economy.

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5 Quantum Computing Stocks Investors Should Be Watching in 2027

For many years, quantum computing was little more than a weird, niche lab project that investors read about, but that relatively few bought into. 2026 changed that. A wave of companies went public through mergers and listings, and names most people ignored a year ago now carry market values in the billions. Some of them build actual quantum computers. Others make the lasers, chips, and security tools that those machines need. Here are five I think deserve a spot on your quantum computing stock list heading into 2027, with what each company is doing and where the trouble spots are. Image source: Getty Images. 1. Quantum Computing Quantum Computing Inc. (QUBT +0.58%) started as a small software operation, but it's a much different business now. Through a series of acquisitions, including Luminar Semiconductor, it runs its own chip manufacturing and offers foundry services for thin-film lithium niobate chips. Those chips transmit data via pulses of light rather than electricity, which increases signal speed and generates less heat. As of its last quarterly report, the company had about $1.3 billion in cash and investments on its books, which gives it some room to keep buying. The concern for investors is that most of its growth has come from those acquisitions. First-quarter revenue jumped from $39,000 to $3.7 million, a rise the company tied to its purchase of Luminar Semiconductor, and it has spent about $180 million in cash on acquisitions so far this year.  The question is how well all of these acquired businesses will fit together and whether customers will pay for the result. QUBT - Quantum Computing$6.91+$0.04 (+0.58%)NASDAQ · As of 4:00 PM ET 10/28/26Premium FeatureMoneyball Superscore45/100Key data pointsMarket capMarket cap calculated using publicly traded shares outstanding only. Does not include unlisted, private, or dual-class non-traded shares. Implied market cap may vary.$1.56BDay's range$6.72 - $6.9452wk range$6.08 - $16.79Volume42.8KAvg volum

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Researchers Curb Unwanted Transitions in Ultrafast Quantum Gates

Researchers Curb Unwanted Transitions in Ultrafast Quantum Gates Matt Swayne October 29, 2026 Research & Science Insider Brief Researchers from Jülich, Aachen and Cologne performed single-qubit operations in 6.8 nanoseconds with accuracies exceeding 99.98%, demonstrating fast, precise control of a superconducting qubit. Their R2D pulse-shaping method reduced unwanted multiphoton transitions by more than 20 times compared with the established DRAG method. Tests used a single isolated transmon qubit, with further experiments needed to assess performance in larger processors and two-qubit operations. Image: Quantum computer laboratory at the Peter Grünberg Institute for Functional Quantum Systems (PGI-13) at Forschungszentrum Jülich. (Forschungszentrum Jülich / Sascha Kreklau) PRESS RELEASE — Researchers from Jülich, Aachen and Cologne have performed a quantum operation on a qubit in just 6.8 nanoseconds – with more than 99.98 percent accuracy. Such operations typically take several tens of nanoseconds in superconducting quantum computers. The paper was published in Nature Communications. 6.8 nanoseconds – that’s how long it takes light to travel a distance of around two metres in a vacuum. In this tiny fraction of a second, researchers in the Peter Grünberg Institute for Functional Quantum Systems (PGI-13) and Quantum Control (PGI-8) at Forschungszentrum Jülich have succeeded in performing elementary computing operations on a quantum bit, or qubit for short. Switching speed is a critical factor in quantum computing. This is because qubits are sensitive and retain their quantum states for only a limited time. The faster individual operations can be performed, the more can fit into this time window. This is important for complex algorithms and for resource-intensive error correction. However, the shorter the microwave pulses used to switch qubits, the stronger they must be. This increases the risk of unwanted excitation. This is because superconductin

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UK Space Agency and DSIT Publish Quantum Technology Roadmap for Space Targeting 2035–2040 Sovereign Capabilities

The UK Space Agency (UKSA) and the Department for Science, Innovation and Technology (DSIT) have published the Quantum Technology Roadmap for Space, establishing strategic priorities to guide public capital, qualification pathways, and industrial flight demonstrations through the 2035–2040 horizon. Prepared by Space Professionals Partnership Ltd and Quantum Technologies Associates Ltd, the policy framework aligns the UK’s National Space Strategy with its £2.5 billion, ten-year National Quantum Strategy. The roadmap identifies four primary investment themes—assured timing, resilient sensing, sovereign communications, and long-term computing—designed to transition domestic laboratory prototypes into radiation-tolerant, flight-qualified space systems amid accelerating global state investments exceeding £43 billion across North America, Europe, and Asia. Four Strategic Investment Themes and Space-Qualification Trajectories The roadmap addresses five operational space drivers where classical payloads face physical limitations: assured resilience in congested orbits, drift-free precision sensing, high-throughput distributed architectures, strict Size, Weight, and Power (SWaP) constraints, and autonomous deep-space operations. Rather than treating all quantum modalities equally, the report establishes a phased deployment matrix distinguishing near-term insertable systems from formative, long-horizon technologies: UK Quantum Space Investment Themes and Deployment HorizonsInvestment ThemePriority TechnologiesOperational Role & Target HorizonAssured Timing & SynchronisationTrapped-ion optical clocks, room-temperature vapor clocks, chip-scale optical clocks, and Quantum Time Transfer (QTT).Provides GNSS-independent sovereign orbit determination, satellite cross-link synchronisation, and ultra-stable ground-space time distribution (5–10 year pathfinder LEO flights).Resilient Position, Navigation & SensingCold-atom accelerometers/gravimeters, Optically Pumped Magneto

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Pasqal 256-Qubit Neutral-Atom Processor Observes Unpredicted Slowdown in 2D Quantum Magnet Dynamics

Pasqal, in collaboration with the Flatiron Institute’s Center for Computational Quantum Physics, the University of Augsburg, Forschungszentrum Jülich, the University of Regensburg, and the Institut d’Optique–CNRS, has mapped the non-equilibrium relaxation landscape of a two-dimensional transverse-field Ising model (TFIM) on a 256-qubit analog neutral-atom processor. Detailed in a preprint titled Exploring the Relaxation Landscape of a 2D Quantum Magnet on a 256-Qubit Processor and an accompanying technical analysis, the study investigates post-quench dynamics following a sudden Hamiltonian parameter shift. The experiments uncover an anomalous relaxation slowdown in a parameter regime where three state-of-the-art classical tensor-network algorithms diverge from one another and fail to maintain controlled late-time predictions. Rydberg Lattice Quenches and Three Non-Equilibrium Relaxation Regimes The experimental architecture configured 256 neutral rubidium-87 (87Rb) atoms into a 16×16 square array with nearest-neighbor distance R and van der Waals interactions decaying as U(R/rij)6. Ground and Rydberg electronic states encoded the pseudospin degrees of freedom, driven by global two-photon laser excitation with Rabi frequency Ω and detuning δ to simulate ferromagnetic Ising interactions under a transverse field hx. Initializing the processor in a fully polarized product state |↓···↓⟩, researchers executed an abrupt quantum quench across varying ratios of hx/J. The resulting dynamics revealed three distinct physical behaviors: rapid thermalization near the dynamical phase transition (hx/J ≈ 2.5), a long-lived prethermal plateau at large transverse fields (hx/J = 6.0) governed by an emergent U(1)-symmetric XY Floquet Hamiltonian (HZY), and an unexpected relaxation slowdown at intermediate field strengths (hx/J = 3.5). Dynamical Regimes and Classical Simulation Benchmarks on Pasqal 256-Qubit ArrayField Parameter RegimePhysical Behavior Observed on QPUTheoretical & Co

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BlueQubit Benchmarks Quantum Computational Advantage in Random-Circuit Sampling on IBM Nighthawk r2 Hardware

Fidelity estimation and main result. Researchers from BlueQubit, in collaboration with the Ecole Polytechnique Fédérale de Lausanne (EPFL) and the XPRIZE Foundation, have reported a demonstration of quantum computational advantage via random-circuit sampling (RCS) on commercial superconducting hardware. In a preprint titled Quantum computational advantage in random-circuit sampling on IBM superconducting quantum computers, the team executed forward RCS on IBM Quantum’s 120-qubit ibm_phoenix processor, which uses the square-lattice Nighthawk r2 architecture. Operating over 61 active qubits using native controlled-Z (CZ) gates, the circuits were submitted through standard Qiskit Runtime cloud queues without bespoke hardware calibration, yielding an output ensemble that would require over a century to sample classically under baseline rejection-sampling assumptions. Circuit Construction, Dual Fidelity Verification, and Contraction Complexity The experiment deployed a four-color brickwork schedule (cycling through matchings A, B, C, and D) across 102 active couplers, averaging 25.5 native CZ operations per cycle. Rather than relying solely on extrapolation, the team established fidelity across depths ranging from 4 to 40 cycles using two independent methodologies: mirror benchmarking with randomized compiling and multi-partition cross-entropy benchmarking (XEB) across three and four balanced patches. At the anchor depth of 36 cycles (incorporating 918 CZ gates), where tensor-network contraction costs saturate at full system size with a width of w = 64, the experimental fidelity reached FXEB ≈ 2.3 × 10−3. While the ibm_phoenix processor collected 106 samples in 19 seconds of active quantum processing unit (QPU) time, reproducing that ensemble via tensor-network rejection sampling on the Oak Ridge National Laboratory Frontier supercomputer would require an estimated 1.2 × 1027 machine floating-point operations (FLOPs)—amounting to roughly 110 years of exascale compute. 61

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Gothenburg and Chalmers Researchers Develop Modular Graph Neural Decoder for Surface Code Memory and Lattice Surgery

Researchers from the University of Gothenburg and Chalmers University of Technology have demonstrated a modular neural decoder for surface code quantum error correction (QEC) capable of transferring across hardware architectures, variable noise profiles, and multi-patch logical operations. In a preprint titled Modular Neural Decoder for Surface Code Memory and Logic on Superconducting Processors, the team validated the architecture on superconducting hardware data from IBM Quantum’s Nighthawk r1 generation (ibm_berlin) and Google Quantum AI’s below-threshold Sycamore processor. The framework outperforms minimum-weight perfect matching (MWPM) on memory experiments and successfully decodes a real two-patch ZZ-merge lattice-surgery operation while remaining trainable on a single commercial GPU. Renormalization-Inspired Graph Architecture and Experimental Decoding Milestones State-of-the-art neural decoders such as AlphaQubit rely on dense recurrent Transformer blocks that require extensive high-performance computing clusters to train and scale with prohibitive O(d4) attention bottlenecks across code distance d. To circumvent model bloat, the Swedish team designed a hierarchical architecture inspired by the renormalization group: a shared local graph neural network (GNN) processes small 3×3×2 space-time clusters of syndrome detection events, which are subsequently coarse-grained via lightweight convolutional aggregation layers and integrated across time by a gated recurrent unit (GRU). Because scaling to larger code distances adds only small convolutional layers rather than expanding the primary neural backbone, the total parameter count remains bounded at roughly two to three million parameters across distances d = 3, 5, 7, and 9. Modular Neural Decoder Experimental Benchmarks across QEC RegimesExperimental BenchmarkHardware & Dataset PlatformDemonstrated Decoding PerformancePlanar Memory (d = 3 & d = 5)IBM Quantum (ibm_berlin Nighthawk r1)Halves logical error

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Yonsei University Researchers Develop Mamba State-Space Neural Decoder to Enable Real-Time Quantum Error Correction

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Yonsei University Researchers Develop Mamba State-Space Neural Decoder to Enable Real-Time Quantum Error Correction

The neural decoder comprises a stabilizer embedder, a recurrent core, a syndrome mixer, and a readout network. Researchers from Yonsei University have developed a neural quantum error correction (QEC) decoder based on the Mamba structured state-space architecture, reducing the computational complexity of deep-learning decoding from O(d4) to O(d2) in code distance d. In peer-reviewed research published in npj Quantum Information, the team demonstrated that the state-space model matches the accuracy of Transformer-based models such as AlphaQubit on experimental memory benchmarks while eliminating the quadratic self-attention bottlenecks that prevent real-time feedback on physical quantum hardware. State-Space Syndrome Processing and Real-Time Decoding Metrics In planar surface codes, syndrome extraction generates sequences scaling with the number of stabilizers (Nstab = d2 − 1). Standard Transformer neural decoders utilize multi-head self-attention mechanisms that perform pairwise comparisons between all stabilizer tokens, yielding an O(d4) scaling that accumulates latency backlogs well beyond microsecond superconducting coherence limits. The Mamba decoder replaces explicit multi-head attention with selective state-space layers, utilizing input-dependent linear scans to maintain historical syndrome contexts within compressed hidden states. Evaluated on the Google Quantum AI Sycamore hardware dataset, the state-space architecture matched Transformer accuracy in static memory benchmarks and maintained an error-suppression advantage when accounting for decoder-induced latency noise. Decoding Performance and Scaling Metrics across Surface Code BenchmarksEvaluation MetricMamba DecoderTransformer Decoder (AlphaQubit Baseline)Core Computational ComplexityO(d2) (Linear in syndrome tokens)O(d4) (Quadratic pairwise self-attention)Sycamore Memory LER (d = 3)2.981 × 10−2∼ 2.98 × 10−2Sycamore Memory LER (d = 5)2.916 × 10−2∼ 2.91 × 10−2Real-Time LER at d = 9 (With Latency Noise)1.5

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University of Sydney Demonstrates Multi-Qubit Logical Entanglement and 170-Round Stability on IBM Square-Lattice Nighthawk r2 Processor

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University of Sydney Demonstrates Multi-Qubit Logical Entanglement and 170-Round Stability on IBM Square-Lattice Nighthawk r2 Processor

Researchers from the University of Sydney have demonstrated multi-qubit fault-tolerant logic and sustained error suppression on IBM Quantum‘s cloud-deployed ibm_phoenix processor, a 120-qubit superconducting system based on the square-lattice Nighthawk r2 architecture. The experimental findings, reported in an arXiv preprint (arXiv:2610.32347) and highlighted by IBM Research, validate distance-3 and distance-5 rotated surface code memories, logical Bell-state preparation via lattice surgery, and state and gate teleportation across three logical qubits. The results mark the first major multi-qubit logical benchmark executed on the platform just 54 days following its fleet integration. Dynamic Role Alternation and Multi-Round Stability Benchmarks A primary bottleneck in multi-round superconducting quantum error correction (QEC) is population leakage into non-computational states (such as the |2⟩ transmon state). While ancilla qubits are reset following syndrome measurements, data qubits remain unmeasured during stabilizer cycles, allowing leakage errors to accumulate and saturate error-suppression scaling. To circumvent this degradation, the University of Sydney team implemented a dynamic “walking” syndrome circuit that alternates the physical roles of data and ancilla qubits on successive rounds. By swapping functional roles at each time step, every physical qubit across the active patch undergoes measurement and hardware reset every two rounds, clearing non-computational leakage states before they spread into uncorrectable correlated logical faults. University of Sydney QEC Experimental Benchmarks on ibm_phoenixOperational MetricDemonstrated ValueFault-Tolerant System SignificanceProcessor Architectureibm_phoenix (120-qubit Nighthawk r2)Features square-lattice planar connectivity and fast hardware active reset.Memory Error per Round< 1% (Distances d = 3 and d = 5)Maintains logical memory suppression below fault-tolerant thresholds.Logical Bell-State Fidelity>

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Nord Quantique Secures $10 Million DARPA Quantum Benchmarking Initiative Stage B Agreement Extension

Nord Quantique has secured a $10 million agreement extension under Stage B of the Defense Advanced Research Projects Agency (DARPA) Quantum Benchmarking Initiative (QBI), building on its earlier selection when DARPA advanced eleven teams to Stage B. The award finances ongoing engineering milestones and physical risk-mitigation prototyping for the company’s bosonic quantum error correction (QEC) architecture. DARPA’s QBI program is structured to evaluate whether emerging quantum computing hardware platforms can achieve utility-scale operational performance—defined as computational utility exceeding implementation cost—by 2033 through rigorous independent verification and validation (IV&V) metrics. Superconducting Bosonic Codes and Hardware-Level Fault-Tolerance Benchmarks Unlike conventional discrete-variable solid-state architectures that require hundreds or thousands of physical transmons to assemble a single fault-tolerant logical qubit via surface or LDPC codes, Nord Quantique employs continuous-variable bosonic codes embedded in superconducting microwave cavities. As detailed in previous technical evaluations of Nord Quantique’s GKP reservoir engineering, the architecture encodes quantum information into multiphoton states to implement active error correction directly at the physical layer, targeting an effective 1:1 physical-to-logical footprint. Under Stage B of the DARPA protocol, performers must deliver comprehensive research and development execution plans, identify dominant decoherence channels—including photon loss and phase-space diffusion—and demonstrate prototype hardware subsystems that actively reduce state preparation and measurement (SPAM) and gate error rates below fault-tolerant thresholds. DARPA QBI Program Structure and Nord Quantique Evaluation ScopeProgram StageFunding & Award ParametersTechnical Mandate & Verification MilestonesStage A (Feasibility)Awards up to $1 MillionInitial conceptual system design and theoretical derivation

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LANL and Cornell Researchers Demonstrate Extraction of Quantum Information Structures from Condensed-Matter Dynamical Simulations

Schematic representation of our framework. Researchers at Los Alamos National Laboratory (LANL) and Cornell University have published an analytical and numerical framework demonstrating that intermediate many-body states prepared during dynamical quantum simulations can be repurposed to interrogate quantum information metrics. In a preprint titled Dynamical quantum simulations as laboratories for quantum information, the team analyzed local-pulse protocols traditionally executed on quantum hardware to calculate retarded spin Green functions and inelastic neutron scattering dynamical structure factors. By reframing opposite local spin rotations as the encoding of a classical bit or a continuous parameter into evolving many-body states, the study tracks how quantum information propagates, reorganizes across spatial partitions, and responds to physical measurement constraints in 50-spin XX and XXX (isotropic Heisenberg) chains. Information Propagation, Representation-Readout Inversion, and Multipartite Entanglement The investigation establishes that conventional local response functions capture only a restricted projection of the underlying quantum record. As the spatial observation aperture expands from a single site (k = 1) to seven sites (k = 7), the trace distance ΔR and Holevo information χR reveal persistent multi-site correlations that remain present even when local single-spin response functions vanish. A central finding is an inversion between mathematical representation complexity and physical measurement efficiency: while the free-fermionic XX chain admits an exact, compact description via two-point fermionic covariance matrices, simple product-Pauli spin measurements recover a larger fraction of available distinguishability, Holevo information, and quantum Fisher information (QFI) in the strongly interacting XXX chain. Furthermore, the prepared dynamical states allow direct certification of evolving quantum resources, demonstrating that localized perturbati

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EPB and IonQ Deploy Hybrid Trapped-Ion Algorithms on Forte Enterprise to Optimize Power Grid Topology

Municipal utility EPB, in collaboration with IonQ, has published experimental findings across three technical preprints demonstrating hybrid quantum-classical algorithms for distribution network reconfiguration (DNR). Funded under a $4 million grant from the National Institute of Standards and Technology (NIST), the project deployed EPB’s in-house Quantum Computing Fellows to execute optimization protocols on an on-premises IonQ Forte Enterprise trapped-ion system at the EPB Quantum Center in Chattanooga, Tennessee. The research benchmarks three distinct computational frameworks targeting active line-loss reduction and real-time switch allocation across distribution networks spanning up to 417 electrical buses. Constraint-Preserving Mathematical Encodings and Hardware Implementations DNR is a mixed-integer nonlinear programming challenge that requires selecting sectionalizing and tie switch states to minimize resistive dissipation while maintaining radial tree topologies without closed loops. Conventional quantum optimization approaches enforce radiality via penalty terms in Quadratic Unconstrained Binary Optimization (QUBO) matrices, introducing artificial energy barriers and barren plateaus. To bypass these issues, the joint research teams implemented three topology-preserving architectures directly on trapped-ion hardware: Technical Methodologies and Experimental Benchmarks across EPB-IonQ PreprintsComputational ArchitectureMathematical & Algorithmic EncodingDemonstrated Hardware & Simulation PerformanceQuantum-Enhanced Deep Reinforcement Learning (Q-DRL)Double Deep Q-Network (Double DQN) with a 4-layer Parameterized Quantum Circuit (PQC) backbone and double decision-head masking.Achieved 95.8% parameter reduction on the IEEE 33-bus system (9,156 parameters vs. 220,052 classically); IonQ Forte Enterprise execution yielded exact optimum (0.1396 MW loss) for 33 buses and within 0.30% of optimum for 69 buses.Subspace LR-QAOA with Guided MISOCP SolverCycle-ed

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NQCP Spins Out Pelagic Systems to Commercialize Real-Time Hardware Calibration Software

The Novo Nordisk Foundation Quantum Computing Programme (NQCP) at the Niels Bohr Institute, University of Copenhagen, has spun out Pelagic Systems to commercialize real-time control, calibration, and characterization software for quantum processors. Developed and validated on superconducting quantum hardware at NQCP and supported by non-dilutive grant capital from Innovation Fund Denmark via its InnoExplorer and Grand Solutions programs, the platform addresses runtime parameter instabilities—such as qubit frequency drift, fluctuating coherence lifetimes, and readout degradation—that constrain multi-qubit system performance. Real-Time FPGA Control Loops and Quantum Characterization Architecture Traditional quantum processor characterization relies on host-mediated feedback loops: measurement data generated at the quantum processing unit (QPU) travels to a host CPU for analysis, which then issues updated pulse configurations back to the instrument rack, incurring latency penalties typically on the order of 250 ms for standard sweeps. Pelagic Systems shifts parameter estimation and feedback decision-making directly into the real-time execution loop by running routines locally on FPGA controllers. On operational superconducting hardware, this on-controller estimation pipeline measures relaxation times (T1) and executes recalibration sweeps in 9.8 ms, enabling adaptive multi-qubit operations and granular tracking of fast noise dynamics. The broader platform combines this low-latency control with an extensible experimental runtime and automated Quantum Characterization, Verification, and Validation (QCVV) frameworks designed to interface with heterogeneous hardware platforms. Commercial Positioning and Scaling Realities in Quantum Control Software The launch of Pelagic Systems targets a growing bottleneck as quantum hardware transitions from isolated academic physics experiments to complex multi-qubit architectures that require continuous, automated tuning. While on-FPGA

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