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Quantum Machine Learning: QML Algorithms & Quantum AI Applications

Quantum machine learning news: QML algorithms, quantum AI, quantum neural networks. Hybrid quantum-classical ML & quantum advantage research.

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Quantum machine learning (QML) explores intersections between quantum computing and artificial intelligence, investigating whether quantum algorithms can accelerate data analysis, pattern recognition, and model training beyond classical capabilities.

Theoretical foundations include quantum advantages for linear algebra subroutines central to machine learning—matrix inversion, principal component analysis, and vector inner products. The HHL algorithm promises exponential speedup for specific sparse, well-conditioned systems.

India's Quantum Machine Learning Landscape

India's National Quantum Mission supports quantum machine learning research through its Quantum Computing Thematic Hub at IISc Bengaluru. The Indian Institute of Science offers a Certificate Programme in Quantum Computing and Artificial Intelligence through its Centre for Continuing Education, providing comprehensive training in quantum AI applications with hands-on coding using Qiskit and PennyLane.

Tata Consultancy Services (TCS) develops quantum machine learning algorithms for enterprise applications. Infosys explores quantum AI through its Quantum Living Labs. IIT Delhi offers certification programs in quantum computing and machine learning in collaboration with industry partners.

The NQM targets developing quantum algorithms for optimization, simulation, and machine learning, with human resource development including training programs for quantum professionals.

Current NISQ-era QML relies on hybrid quantum-classical approaches including variational quantum algorithms, quantum neural networks, and quantum kernel methods. Challenges include "barren plateaus" in optimization landscapes limiting trainability, and limited qubit counts restricting model complexity.

Three-uniform edge-ordered hypergraph quantum states: entanglement and its relation to hypergraph propertiesquantum-computing

Three-uniform edge-ordered hypergraph quantum states: entanglement and its relation to hypergraph properties

--> Quantum Physics arXiv:2609.30399 (quant-ph) [Submitted on 24 Sep 2026] Title:Three-uniform edge-ordered hypergraph quantum states: entanglement and its relation to hypergraph properties Authors:N. A. Susulovska, Kh. P. Gnatenko View a PDF of the paper titled Three-uniform edge-ordered hypergraph quantum states: entanglement and its relation to hypergraph properties, by N. A. Susulovska and 1 other authors View PDF HTML (experimental) Abstract:The encoding of 3-uniform edge-ordered weighted hypergraphs into multiqubit quantum states is proposed. We derive an analytical expression for the geometric measure of entanglement of hypergraph quantum states and establish its relation to hypergraph properties. The results establish a direct connection between the local structure of hypergraphs, hyperedge weights, and the entanglement of the corresponding quantum states. For equally weighted hypergraphs, it is shown that the entanglement depends explicitly on the vertex degree. The dependence of entanglement on hypergraph parameters is studied analytically and using quantum computing. Hypergraph states associated with chain, star, regular lattice, and binary-tree hypergraphs are examined, and their entanglement is quantified using quantum computing. These results open up the possibility of studying hypergraph properties with quantum programming. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2609.30399 [quant-ph]   (or arXiv:2609.30399v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2609.30399 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Nataliia Susulovska [view email] [v1] Thu, 24 Sep 2026 18:04:35 UTC (7,410 KB) Full-text links: Access Paper: View a PDF of the paper titled Three-uniform edge-ordered hypergraph quantum states: entanglement and its relation to hypergraph properties, by N. A. Susulovska and 1 other authorsView PDFHTML (experimental)TeX Source view license Current

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Generation of Photonic Graph States with minimal number of quantum emittersquantum-computing

Generation of Photonic Graph States with minimal number of quantum emitters

--> Quantum Physics arXiv:2609.30400 (quant-ph) [Submitted on 24 Sep 2026] Title:Generation of Photonic Graph States with minimal number of quantum emitters Authors:Konstantinos-Rafail Revis, Nils Tomke Ottink, Pierre-Emmanuel Emeriau, Paul Hilaire View a PDF of the paper titled Generation of Photonic Graph States with minimal number of quantum emitters, by Konstantinos-Rafail Revis and 3 other authors View PDF HTML (experimental) Abstract:Graph states are a fundamental resource for measurement and fusion-based quantum computing, quantum networks, and sensing. Preparing them in a photonic system deterministically is, in principle, possible, but finding efficient schemes to prepare them was a long-standing problem addressed recently. Additionally, heuristic optimization schemes for reducing the required number of two-qubit gates were developed. However, the problem of reducing the number of emitters by optimizing the emission ordering was not addressed, due to its computational complexity, as it is connected to a well-known NP-hard problem from graph theory, the linear rank width computation. In this work, we focus on developing heuristic polynomial algorithms to reduce the number of emitters required. In total, we propose four distinct algorithms, which demonstrate up to $30\%$ emitter reduction on random graphs. Furthermore, we provide numerical and statistical evidence that the combination of our optimization schemes with the preexisting algorithms for optimizing the two-qubit gates of the preparation protocol can further reduce them by around $20\%$. Finally, we examine the developed algorithms for various useful graph state families, such as graphs useful for measurement-based quantum algorithms, and cluster states and graph codes used for quantum error correction, to determine the performance of each algorithm. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2609.30400 [quant-ph]   (or arXiv:2609.30400v1 [quant-ph] for this version)   ht

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Disentangling Expressibility, Symmetry Protection, and Hardware Noise in Variational Quantum Simulation of the Two-Flavor Schwinger Modelquantum-computing

Disentangling Expressibility, Symmetry Protection, and Hardware Noise in Variational Quantum Simulation of the Two-Flavor Schwinger Model

--> Quantum Physics arXiv:2609.30496 (quant-ph) [Submitted on 24 Sep 2026] Title:Disentangling Expressibility, Symmetry Protection, and Hardware Noise in Variational Quantum Simulation of the Two-Flavor Schwinger Model Authors:Karthikeya Machiraju, Krishna Sujith, Kaustav Bhowmick View a PDF of the paper titled Disentangling Expressibility, Symmetry Protection, and Hardware Noise in Variational Quantum Simulation of the Two-Flavor Schwinger Model, by Karthikeya Machiraju and 2 other authors View PDF HTML (experimental) Abstract:Existing quantum simulations of the two-flavor Schwinger model have run at a single lattice size, and it is not known how far the variational approach can be pushed or which weakness stops it first. Following the model from N = 2 to 6 staggered lattice sites, we find that the binding constraint at reachable sizes is hardware noise rather than circuit expressibility or trainability, and identify N = 3 as the immediately viable extension of existing trapped-ion experiments. The energy error of a charge-conserving ansatz collapses onto one function of p/d, the ratio of variational parameters to physical-sector dimension, and falls by more than two orders of magnitude as p/d rises through order unity, giving the expressibility condition L(4N - 1) >= binom(2N,N) for L circuit layers. The condition is local in chemical potential: at N = 3 the layer count sufficient at zero chemical potential leaves a 74.38% error near the first-order boundary, while one further layer reaches 0.08%. Charge conservation also protects trainability and prevents charge-sector leakage: as the qubit count doubles from 4 to 8, the normalized gradient variance falls to 1/3.56 of its starting value for the constrained ansatz, versus 1/13.57 for an unconstrained circuit. Comparing a global contraction with per-gate local noise, a fixed-parameter control shows that the noise model, not whether the optimizer runs inside the noisy loop, sets how strongly noise degrades the fi

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Encryptability As a Coordinate Choice: Depth-One Homomorphic Federated Learning of Quantum Neural Networksquantum-computing

Encryptability As a Coordinate Choice: Depth-One Homomorphic Federated Learning of Quantum Neural Networks

--> Quantum Physics arXiv:2609.30581 (quant-ph) [Submitted on 24 Sep 2026] Title:Encryptability As a Coordinate Choice: Depth-One Homomorphic Federated Learning of Quantum Neural Networks Authors:Marcel Mordarski, Nathan Mani, Arshad Patel, William Knottenbelt, Roberto Bondesan View a PDF of the paper titled Encryptability As a Coordinate Choice: Depth-One Homomorphic Federated Learning of Quantum Neural Networks, by Marcel Mordarski and 4 other authors View PDF HTML (experimental) Abstract:Encrypted training relies on keeping server-side updates low-degree. This constraint traditionally excludes models whose weights inhabit a compact Lie group (notably variational quantum circuits, where every trainable weight is an $\mathrm{SU(2)}$ rotation). Expressed in Euler angles or discrete alphabets, these updates appear transcendental, historically demanding prohibitive costs: one client--server round per gate, or upwards of $25{,}000$ operations per weight. This penalty is strictly an artefact of coordinates. In the unit-quaternion (spin) chart, group composition is exactly bilinear (degree two, with coefficients in $\{-1,0,+1\}$). Consequently, encrypted rotation updates cost one multiplicative level and federated averaging costs zero in any levelled homomorphic scheme, completely eliminating bootstrapping. This implementation-independent algebraic property is confirmed across two cryptographic backends, introducing only $0.0$ and $-2.0\times10^{-12}$ rad of aggregation error. Leveraging this reduction yields a non-interactive protocol for encrypted federated training of hybrid quantum--classical networks. It includes correctness proofs for aggregation and sign handling, plus a compilation lemma proving parameterised entanglers add only constant-factor overhead without altering the depth class. Empirically, a paired five-seed study confirms zero measurable utility tax ($\Delta=+9\times10^{-6}$ MSE, $p=0.92$), and a noise-budget ablation falsifies the hypothesis that encr

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Topology from disorderquantum-computing

Topology from disorder

Topological phases of matter have long been studied in idealized pure states at absolute zero. Two experiments now show how controlled disorder can give mixed states measurable topological features in quantum simulators. This is a preview of subscription content, access via your institution Access options Access through your institution Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription $32.99 / 30 days cancel any time Learn more Subscribe to this journal Receive 12 print issues and online access $259.00 per year only $21.58 per issue Learn more Rent or buy this article Prices vary by article type from$1.95 to$39.95 Learn more Prices may be subject to local taxes which are calculated during checkout Fig. 1: Emergence of topology and topological phase boundaries in disordered quantum simulators. Subjects Quantum simulation Topological matter Ultracold gases Sensory Ethnography in Urban Anthropology ReferencesYue, Z. et al. Nat. Phys. 22, 844–850 (2026).Article  Google Scholar  Su, L. et al. Nat. Phys. https://doi.org/10.1038/s41567-026-03381-6 (2026).Article  Google Scholar  Senthil, T. Annu. Rev. Condens. Matter Phys. 6, 299–324 (2015).Article  ADS  Google Scholar  Chen, X., Gu, Z.-C., Liu, Z.-X. & Wen, X.-G. Science 338, 1604–1606 (2012).Article  ADS  Google Scholar  Ma, R. & Wang, C. Phys. Rev. X. 13, 031016 (2023). Google Scholar  Preskill, J. Quantum 2, 79 (2018).Article  Google Scholar  Google Quantum AI and Collaborators. Nature 638, 920–926 (2025).Article  ADS  Google Scholar  Evered, S. J. et al. Nature 645, 341–347 (2025).Article  ADS  Google Scholar  Braun, C. et al. Nat. Phys. 20, 1306–1312 (2024).Article  Google Scholar  Yao, R. et al. Nat. Phys. 20, 1726–1731 (2024).Article  Google Scholar  Download referencesAuthor informationAuthors and AffiliationsDepartment of Ph

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Quantum-safe algorithms may fail faster with powerful AI tools From SIKEquantum-computing

Quantum-safe algorithms may fail faster with powerful AI tools From SIKE

A cryptographic algorithm once considered a leading candidate for quantum-resistant encryption fell in just one hour on a standard laptop, not to a quantum computer, but to a mathematical insight from 1997. The Supersonic Multivariates Isogeny Key Encapsulation (SIKE) protocol advanced to the fourth round of evaluation by the National Institute of Standards and Technology before mathematicians Wouter Castryck and Thomas Decru connected its structure to Ernst Kani’s decades-old theorem. This collapse highlights a growing vulnerability, as frontier AI systems now possess the capacity to rapidly explore obscure mathematical connections, potentially shortening the window between overlooked weakness and successful attack, according to security leaders. “Years without a successful attack provide evidence, but they cannot establish that every useful mathematical connection has been explored,” the researchers noted. In May 2026, OpenAI reported that an internal model disproved a longstanding conjecture associated with Erdős’s planar unit-distance problem, first posed in 1946. The model applied sophisticated algebraic number theory to a seemingly elementary geometry question, a transfer of ideas between fields that produced a major result. This month, OpenAI announced an AI-generated solution to the Navier-Stokes existence and smoothness problem, unresolved for roughly 90 years, and released both a written proof and a formalization for computer verification. The announcement remains subject to mathematical scrutiny, but the progression is stunning. Frontier systems are now producing research claims against problems that have occupied generations of the best mathematicians. It is certain these capabilities to accelerate the search for overlooked cryptographic weaknesses are being used on a vast scale, especially by intel agencies with enormous grid-straining compute, long before AI made it cool. Would models have independently broken SIKE in hours?

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Quantum chip predicts time series with a feedback loopquantum-computing

Quantum chip predicts time series with a feedback loop

Nearly four decades passed between the 1971 postulation of the memristor and its eventual demonstration in 2008. The memristor functions as the primary example of neuromorphic components due to its ability to retain memory through hysteresis, mirroring how synapses function in the human brain. This work explores integrating the memristor with quantum computing to create more efficient machine learning platforms. Photonic Quantum Memristor Enables Neuromorphic Computing The implementation of a quantum reservoir computing system using single photon states marks a first for the field, according to work detailed in a recent publication. Researchers designed the device to update its internal phase via feedback based on measurements at one output mode. The reservoir’s output then undergoes processing by a linear regression model, calculating a weighted sum to produce a final result. To test the system’s efficacy, the team addressed four distinct tasks: predicting a smooth nonlinear function and forecasting three random time series, NARMA, Mackey-Glass and Santa Fe, with vowel recognition also numerically simulated. These benchmarks allowed for a direct comparison of performance with and without the quantum memristor’s dynamic enhancements. The photonic quantum memristor itself is modeled as a tunable Mach-Zehnder interferometer, with its internal phase updated by a feedback rule dependent on measurement outcomes. The researchers employed a unitary representation of the memristor action, adaptively updated based on previous outcomes, with coefficients serving as hyperparameters within the model. For time-series prediction, they fixed certain parameters, focusing on the memory decay rate as an adjustable hyperparameter. This design allows the system to exploit the feedback mechanism to implement nonlinear operations on input states and use short-term memory, effectively demonstrating a proof-of-principle quantum reservoir computing system. The team encoded classical data us

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Quantum algorithms gain from filtered-state preparationquantum-computing

Quantum algorithms gain from filtered-state preparation

Researchers from Sungkyunkwan University in South Korea and Xanadu in Canada detailed a new method for improving quantum algorithms in a paper published September 25, 2026, in Quantum Science and Technology. The team developed a technique designed to address a critical limitation in many quantum algorithms: accurate state preparation. This work demonstrates a framework for enhancing the overlap of input states through spectral filtering, potentially reducing runtime by more than two orders of magnitude for certain calculations, with overlap amplification exceeding a factor of one hundred. Filtered Quantum Phase Estimation for Eigenvalue Problems The success probability of quantum phase estimation hinges on initial state overlap; a new framework detailed in Quantum Science and Technology on September 25, 2026, directly addresses this limitation through filtered-state preparation. Researchers from Sungkyunkwan University and Xanadu developed a method to amplify this overlap, potentially reducing the computational cost of determining essential properties of many-body Hamiltonians, such as ground-state energy and excited spectra. The work introduces a unified framework for quantum algorithms centered on enhancing the initial connection with target eigenstates. This framework explicitly defines the trade-off between overlap amplification, the probability of successfully preparing a state, and the resources required to implement the filter itself. Analysis of Gaussian filters and a modified Krylov-subspace-based filter revealed improvements in the success-probability/overlap balance important for preparing states. The team’s approach tackles a central challenge in quantum computing: accurately estimating eigenvalues, a task where standard quantum phase estimation requires a significant initial overlap between the prepared input state and the target eigenstate. Specifically, the success probability of QPE scales with the squared overlap, meaning even small improvements in

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Zapata Quantum’s Sumit Kapur joins Boston’s Power 50 leadersquantum-computing

Zapata Quantum’s Sumit Kapur joins Boston’s Power 50 leaders

Sumit Kapur, chief executive of Boston-based quantum software company Zapata Quantum, has been named to the Boston Business Journal’s 2026 Power 50: Movement Makers list. The recognition follows a two-year period where Kapur led a restructuring focused on developing applications to run on rapidly advancing quantum hardware. “I am honored to be recognized alongside so many outstanding leaders in Boston,” said Kapur, adding that the achievement reflects Zapata Quantum’s origins in “the long arc of innovation that began in Harvard’s quantum computing lab and The Engine built by MIT.” Kapur’s Restructuring Positions Zapata for Quantum Application Leadership The company, which regained SEC reporting status and now trades on the OTCQB Venture Market under the ticker ZPTA, completed a $15 million financing round in April 2026 as the final step in this strategic shift, bringing total funding to $93 million. This capital infusion followed earlier Series An and B rounds in 2021 and 2023, and a bridge financing in 2025, enabling a rebuilding of the team and deepened industry partnerships, Zapata Quantum says. Kapur’s leadership prioritized re-integrating founding expertise from Harvard’s quantum computing lab, alongside new hires from established technology firms like Palantir, Oracle and Northrop Grumman. This blend of academic rigor and commercial experience was designed to accelerate the development of quantum software applications, addressing a critical bottleneck as quantum hardware rapidly advances. The company’s collaborative work with NVIDIA, using agentic AI to expedite quantum algorithm discovery, exemplifies this application-focused approach. Zapata’s Quantum Pilot platform, launched in early access on September 22, further demonstrates this commitment by providing enterprises with tools to evaluate the potential of quantum solutions. The impact of this restructuring extends beyond internal development, as evidenced by recognition of Zapata’s research with the Dana-

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Quantum computer simulates matter “popping into existence” - ScienceDailyquantum-computing

Quantum computer simulates matter “popping into existence” - ScienceDaily

Science News from research organizations Quantum computer simulates matter “popping into existence” Scientists used a quantum computer to simulate particles seemingly “popping into existence,” opening a new window into the physics of the early universe. Date: September 26, 2026 Source: Duke University Summary: Scientists recreated a particle-forming process linked to the extreme physics of the early universe using a 13-ion quantum simulator. The breakthrough suggests quantum computers could eventually help researchers investigate how matter formed and evolved after the Big Bang. Share: Facebook Twitter Pinterest LinkedIN Email FULL STORY Researchers have observed string-breaking dynamics on a quantum simulator that could help probe questions related to the Big Bang. This is an artistic rendering of string-breaking. Credit: Emily Edwards, Duke University Researchers led by the Duke Quantum Center (DQC) have used a quantum simulator to observe string breaking dynamics connected to particle antiparticle formation, marking one of the earliest demonstrations of its kind in quantum physics. The work, published September 23 in Nature Physics, shows how trapped ion quantum computers could become powerful tools for exploring some of the deepest questions in fundamental physics. The experiment simulated a process known as string breaking, in which two connected building blocks of matter are pulled apart until so much energy accumulates that new particles can effectively "pop into existence" when the connection breaks. "Quantum computer simulations provide the best platform to investigate complex questions like matter formation, short of having witnessed the Big Bang itself," said Christopher Monroe, the Gilhuly Family Presidential Distinguished Professor of Electrical and Computer Engineering and Physics at Duke, who led this research. "These findings signal a marked development in the quantum science field and open new avenues for us to understand string-breaking dynamics."

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CGI Federal to research AI and quantum for Defense Logisticsquantum-computing

CGI Federal to research AI and quantum for Defense Logistics

CGI Federal has entered into an applied research agreement with the U.S, the company says. Defense Logistics Agency and the University of Tennessee, Knoxville to explore agentic AI and quantum computing for logistics resilience. The initiative, called Quantum Pathfinder, aims to move these technologies “from promise to practice” within the Defense Logistics Agency’s systems, uniting government needs with academic research and commercial capability. “Quantum computing will not arrive as a single product launch; it will arrive as a series of hard research problems solved against real mission constraints,” said Victor Foulk, Vice-President of Emerging Technologies at CGI Federal. Initial exploration will focus on warehouse operations, reverse logistics, and inventory positioning for contested environments. DLA, CGI Federal, and UT Knoxville Launch Quantum Pathfinder Research CGI Federal will host the Quantum Pathfinder initiative at its onshore delivery center in Knoxville, Tennessee, directly linking research efforts to a specific geographic hub for quantum innovation. This strategic location reflects a collaborative aim to bolster East Tennessee’s growing reputation as a destination for quantum computing by using existing university and national laboratory resources. The initiative’s initial focus will be on dynamic smart-warehouse orchestration, reverse logistics, and resilient inventory positioning in challenging operational environments, with specific use cases to be determined jointly in the coming months. These areas represent critical logistical challenges for the Defense Logistics Agency, offering a practical testing ground for emerging technologies. CGI Federal intends to build a bridge between current systems and the potential of quantum technology, emphasizing the importance of collaboration with partners who understand both the mission requirements and the underlying research. “We are investing to do that work now, alongside a federal partner that understa

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Quantum attack protection built into CaveroCore for defencequantum-computing

Quantum attack protection built into CaveroCore for defence

Cavero Secure is demonstrating a new approach to hardware security intended to proactively defend against supply chain attacks and emerging quantum threats, the company says. CaveroCore secures completed devices even before shipment, allowing for secure activation in the field using a network of trusted devices; the system identifies cloned, counterfeited, or compromised components. This capability extends quantum attack protection to even the most constrained devices, replacing vulnerable static keys. The company states it is seeking partners and investors to help develop trust protocols for defence technology as it participates in Tech Tour Quantum and Defence in Berlin. CaveroCore Secures Defence Hardware Throughout Supply Chains This proactive approach contrasts with conventional methods focused on threat detection after deployment, offering an advantage in securing sensitive technologies. The system flags cloned or counterfeited devices and components, addressing a vulnerability within complex supply chains. This capability extends quantum-safe security to environments previously considered impractical for such protection. Cavero Secure intends to foster collaboration at the Tech Tour Quantum and Defence event in Berlin, seeking investment to accelerate development of these trust protocols. The company’s factory-to-field solution aims to establish a secure chain of custody for mission-critical technology, protecting it from compromise throughout its lifecycle. Source: https://caverosecure.com/insights/tech-tour-quantum-and-defence-berlin More like thisQuantum Computing Business NewsSplendor Labs launches blockchain built to withstand quantum attacksQuantum AlgorithmsDecaQ achieves 2.045-second median for complex quantum workloadQuantum HardwareInfleqtion Entangles 30 Logical Qubits on Sqale ComputerQuantum Computing Business NewsQTREX Quantum’s AME segment drives $1.55M in first halfStay currentSee today’s quantum computing news on Quantum Zeitgeist for the lat

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NIST Standards Drive Demand for 11 Quantum Encryption Approachesquantum-computing

NIST Standards Drive Demand for 11 Quantum Encryption Approaches

Organizations are now prioritizing evaluation of quantum-resistant encryption solutions as finalized government standards and emerging data interception risks demand immediate action in 2026. The core math underlying today’s standard security frameworks, relied upon for web traffic, cloud workloads, and digital identities, will not withstand the processing capabilities of quantum hardware running Shor’s algorithm. Instead of prime factorization, these defenses utilize lattice-based mathematics, hash structures, or physical laws to secure data, with algorithms like Module Learning With Errors (M-LWE) creating complex equations that resist both supercomputers and quantum systems. Transitioning to these methods, and implementing ML-KEM encryption under NIST post-quantum standards, is essential for enterprise security teams. What is the best quantum-resistant encryption solution for enterprises? To achieve robust, future-proof security, enterprises should prioritize crypto-agility platforms, systems designed to seamlessly integrate and update cryptographic algorithms. These platforms combine automated discovery of vulnerable systems, support for emerging standards, and native hybrid cryptography, allowing organizations to adapt quickly to evolving threats. A solution like enQase enables centralized management of quantum security policies and algorithm updates without disrupting existing software workflows, a critical feature for maintaining operational continuity. NIST subsequently selected HQC as a backup key encapsulation mechanism in March 2025, further solidifying the selection of approved algorithms. Western Digital’s Ultrastar HDDs now incorporate hardware-level defense using post-quantum cryptography and NIST-approved algorithms, indicating a trend toward embedding quantum-resistant cryptography directly into data storage solutions. This proactive approach minimizes the risk of data interception, even if encryption is compromised in transit. The agency’s partners

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Great News for IonQ Stock Investors!quantum-computing

Great News for IonQ Stock Investors!

The quantum computing company announced a huge breakthrough. *Stock prices used were the afternoon prices of Sept. 23, 2026. The video was published on Sept. 25, 2026. Read NextSep 25, 2026 •By Chris NeigerRigetti, D-Wave, or IonQ: Which Quantum Stock Has the Best Shot at Survival?Sep 25, 2026 •By Johnny RiceIonQ Has Made Another Quantum Computing Breakthrough: Here's What a $1,000 Investment in It Could Look Like in 5 YearsSep 25, 2026 •By Will HealyBetter Quantum Computing Stock: Nvidia vs. IonQSep 25, 2026 •By Keithen Drury$500 Invested in IonQ Now Could 10x if Quantum AI Hits by 2029Sep 24, 2026 •By Keith SpeightsIonQ Cleared One of Quantum Computing's Biggest Hurdles. Should You Buy the Stock?Sep 23, 2026 •By Robert IzquierdoBigBear.ai vs. IONQ: Comparing Revenue Trends Between an Artificial Intelligence Upstart and a Quantum Computer CompanyAbout the AuthorA Fool since 2019, and a graduate of Cal State LA with a B.S. in Finance and M.A. in Economics. Parkev is an adjunct professor of Finance and enjoys reading about financial and economic history. You'll often find him writing about stocks in the consumer goods and technology sectors.TMFParkevX@TMFParkevStocks MentionedIonQNYSE: IONQ$45.48(+1.11%)+$0.50Motley Fool Stock Advisor’s Latest PickGet Access---% Avg Return*Average returns of all recommendations since inception. Cost basis and return based on previous market day close.

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Qot Labs Restores VQE Convergence with Error Mitigationquantum-computing

Qot Labs Restores VQE Convergence with Error Mitigation

Combining dynamical decoupling, zero noise extrapolation, and Pauli twirling restores convergence within the ADAPT-VQE algorithm under noisy conditions; this enables more reliable preparation of molecular ground states using quantum computation. Both coherent and incoherent forms of hardware noise impede operator selection, a key step in building the computational circuit, preventing accurate results without mitigation strategies. A vulnerability has been identified within ADAPT-VQE, a quantum computing technique used for finding molecular ground states, relating specifically to how accurately the algorithm chooses computational steps. Predictable and random hardware errors disrupt this key step, hindering its ability to build effective circuits without employing error correction methods. Combining dynamical decoupling, zero noise extrapolation, and Pauli twirling overcomes these issues by restoring reliable operation despite existing imperfections in current technology. Researchers from Virginia Tech and Lake Zurich High School have identified a key weakness within ADAPT-VQE, a quantum computing technique used as a recipe for finding the lowest energy state of a molecule. Inaccuracies during operator selection, akin to choosing specific tools from a set of tools based on what needs fixing, can prevent the algorithm from building effective circuits when faced with both predictable and random hardware errors. These errors distort key calculations needed to guide circuit construction, hindering accurate results without mitigation strategies. Fortunately, combining techniques such as dynamical decoupling, zero noise extrapolation, and Pauli twirling, methods similar to filtering static out of a radio signal or averaging multiple measurements, successfully restores reliable operation despite these imperfections. Mitigation of qubit decoherence enables resilient variational quantum eigensolver calculations Orders-of-magnitude improvements in ADAPT-VQE efficiency were ach

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How to Start Building Quantum Computing Skills in Your Role Today - Built Inquantum-computing

How to Start Building Quantum Computing Skills in Your Role Today - Built In

Image: Shutterstock / Built In REVIEWED BY Seth Wilson Summary: Quantum computing is approaching commercial utility, presenting a potential $2 trillion market opportunity. With adoption bottlenecks shifting from technology to talent, companies are prioritizing workforce upskilling and small-scale testing to secure a competitive edge as hardware scales. more Quantum computing is approaching commercial utility, presenting a potential $2 trillion market opportunity. With adoption bottlenecks shifting from technology to talent, companies are prioritizing workforce upskilling and small-scale testing to secure a competitive edge as hardware scales. The arrival of quantum advantage, the point at which a quantum computer outperforms classical systems at commercially useful tasks, is imminent. Estimates indicate that quantum technology represents a potential $2 trillion market opportunity, with 90 percent of that value projected to accrue to early movers. As a result, more than half of major companies expect to integrate quantum capabilities into their workflows within the next two years. Much like AI skills, understanding quantum computing will become essential for technology literacy, and enterprises are already investing in building internal capability.  Quantum hardware and software technology development is scaling rapidly, but the ultimate bottleneck to adoption won’t be quantum computers themselves. It will be a shortage of people who know how to use and apply their benefits to their fields. For forward-thinking employees, technical experts and business leaders, the immediate priority is understanding what quantum computing can actually achieve today and preparing to tap into it. How Do You Start Building Quantum Computing Skills?Quantum computing learning begins with mastering key concepts like qubits, superposition and entanglement, then identifying whether specific business problems suit quantum algorithms. Learners across all seniority levels can build founda

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CGI Federal, DLA, and University of Tennessee Launch “Quantum Pathfinder” Initiative for Supply Chain Resiliencequantum-computing

CGI Federal, DLA, and University of Tennessee Launch “Quantum Pathfinder” Initiative for Supply Chain Resilience

CGI Federal, DLA, and University of Tennessee Launch “Quantum Pathfinder” Initiative for Supply Chain Resilience U.S. government technology provider CGI Federal Inc., a subsidiary of CGI Inc. (NYSE: GIB), has entered into an applied research and development agreement with the U.S. Defense Logistics Agency (DLA) and the University of Tennessee, Knoxville (UT). The initiative, titled Quantum Pathfinder, explores how agentic artificial intelligence (AI) and quantum computing can optimize advanced warehouse management, reverse logistics, and inventory positioning across contested military environments. Hosted at CGI Federal’s onshore delivery center in Knoxville, Tennessee, the Quantum Pathfinder program bridges federal mission objectives with academic research and commercial quantum capabilities. Rather than testing theoretical demonstrations, the coalition focuses on solving logistical bottlenecks—such as dynamic smart-warehouse orchestration and asset disposition—to strengthen supply chain resilience during operational disruptions. The initiative operates in coordination with the Knoxville Quantum Accelerator and the broader Tennessee Quantum Initiative to foster regional quantum workforce development and commercialization pipelines. [ Quantum Pathfinder Initiative Architecture & Key Stakeholders ]Stakeholder EntityInstitutional RoleResearch Scope & DeliverablesCGI Federal Inc.(Host: Knoxville Onshore Center)• Systems Integrator & Commercialization Lead• Defense, Intelligence & Space Unit• Hybrid Agentic AI & Quantum Workload Integration• Onsite hosting, benchmark publication & PoC executionDefense Logistics Agency (DLA)• Federal Government Mission Owner• U.S. Department of Defense Logistics Hub• Smart-Warehouse Orchestration & Reverse Logistics• Resilient inventory positioning in contested domainsUniversity of Tennessee, Knoxville• Academic Research & Talent Lead• Knoxville Quantum Accelerator• Tennessee Quantum Initiative alignment•

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Researchers Detect Relationships in Quantum Data with 15% Greater Accuracyquantum-computing

Researchers Detect Relationships in Quantum Data with 15% Greater Accuracy

Relationships between states, not individual states, are under investigation. An adaptive relational learning framework is introduced for multiinstance quantum data accessing both pairwise and higher-order relations. The model combines global measurements via SWAP or CYCLE tests for evaluating an n-state Bargmann invariant with shallow trainable transformations applied locally to each input state. Continuous-variable (CV) photonic systems offer natural access to quantum data and necessary computing operations, demonstrating this approach. Tasks solved include hidden relationship detection, geometric phase classification, and sensing in the presence of an unknown shared nuisance. Reduced measurement requirements enable efficient relational learning within photonic quantum computing Perfect test accuracy with just 500 inference shots represents a leap forward in quantum machine learning; previously, comparable results demanded one hundred times more measurements per data point. This breakthrough, achieved by scientists at University of Sheffield using photonic systems, unlocks new possibilities for processing multiple quantum states simultaneously and identifying relationships between them. Their adaptive relational learning framework accesses these connections rather than treating each state independently, enabling complex tasks like hidden relationship detection and geometric phase classification to be performed efficiently. The team’s approach circumvents the rapidly increasing computational cost associated with traditional methods, paving the way for advancements in sensing technologies reliant on analysing interconnected quantum information. Observed improvements averaged ΔA=0.15 over existing continuous-variable classical shadow methods when utilising this framework on tasks involving hidden relationship detection, geometric phase classification, and sensing under shared noise. It is particularly suited to scenarios where solutions are generated by quantum algor

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Quantum computing’s “dark horse” just proved it can go universal - ScienceDailyquantum-computing

Quantum computing’s “dark horse” just proved it can go universal - ScienceDaily

Science News from research organizations Quantum computing’s “dark horse” just proved it can go universal Date: September 25, 2026 Source: University of Chicago Summary: Researchers have shown that exotic quantum particles called non-Abelian anyons can perform the full range of operations needed for universal quantum computing. Using 54 qubits on Quantinuum’s H2 processor, they combined braiding and fusion to unlock capabilities that braiding alone could not provide. Share: Facebook Twitter Pinterest LinkedIN Email FULL STORY Exotic quantum particles just demonstrated a new path to universal, potentially far more efficient quantum computing. Credit: Shutterstock A practical quantum computer must eventually be able to handle any type of quantum algorithm, much like a conventional laptop can run many different kinds of software. Researchers have now demonstrated a new way to reach that level of flexibility using unusual quantum objects known as non-Abelian anyons. Scientists from the University of Chicago Pritzker School of Molecular Engineering (UChicago PME), Harvard, Stony Brook University, and Quantinuum created and tested a full set of operations based on non-Abelian anyons. Their results provide the first experimental demonstration that this approach can support the broad range of operations required for universal quantum computing. "We demonstrated a so-called universal gate set -- meaning that if you store information in these emergent versions of quarks, and you move them around, you can do any quantum computation you might want to do," said Ruben Verresen, assistant professor of molecular engineering at UChicago PME and a co-author of the new study published in Nature. A Possible Shortcut Around Costly Quantum Error Correction The strategy could do more than help create a general-purpose quantum computer. It may also offer a more efficient route toward reliable quantum machines. Quantum computers are extremely vulnerable to errors, so researchers typically p

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Mphasis and Copa Airlines Seek Quantum Boost to Airline Efficiencyquantum-computing

Mphasis and Copa Airlines Seek Quantum Boost to Airline Efficiency

Copa Airlines and Mphasis recently invited teams from Indian Institutes of Technology and the University of Calgary to tackle a precise challenge: optimizing passenger re-accommodation during flight disruptions, the company says. The international competition focused on improving the passenger re-accommodation rate without impacting the solution runtime, areas where current methods fall short. Participants used quantum computing to address a problem stemming from demand shifts, seasonal routes, or cancellations, working with real Copa Airlines data. “The Quantum Computing Challenge reflects Mphasis’ commitment to applied innovation and advancements in quantum technologies,” said Srikumar Ramanathan, Chief Solutions Officer at Mphasis, as industries seek to deploy quantum computing for real-world impact. Mphasis and Copa Airlines Launch Quantum Disruption Management Challenge Teams from the University of Calgary and several Indian Institutes of Technology competed in a challenge designed to refine passenger re-accommodation strategies using quantum computing, a focused invitation indicating a deliberate approach to sourcing solutions. Copa Airlines and Mphasis structured the competition around a specific disruption management issue, arising from factors like fluctuating demand, seasonal route adjustments, or flight cancellations. This precise focus allowed participants to address a well-defined pain point within airline operations, rather than tackling broad, undefined challenges. Mphasis’ commitment to quantum computing extends beyond theoretical exploration, as evidenced by its collaborations and innovation hubs; in September 2024, the company opened a London innovation hub positioned as a center of excellence for quantum computing, quantum cryptography and AI aimed at algorithmic underwriting, catastrophic risk. The company’s NEXT Labs, its research and development division, partnered with Copa Airlines to define the challenge parameters, secure necessary data, an

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