arXiv:2511.18225v1 Announce Type: new 
Abstract: Quantum machine learning seeks to leverage quantum computers to improve upon classical machine learning algorithms. Currently, robust uncertainty quantification methods remain underdeveloped in the quantum domain, despite the critical need for reliable and trustworthy predictions. Recent work has introduced quantum conformal prediction, a framework that produces prediction sets that are guaranteed to contain the true outcome with user-specified probability. In this work, we formalise how the time-varying noise inherent in quantum processors can undermine conformal guarantees, even when calibration and test data are exchangeable. To address this challenge, we draw on Adaptive Conformal Inference, a method which maintains validity over time via repeated recalibration. We introduce Adaptive Quantum Conformal Prediction (AQCP), an algorithm which preserves asymptotic average coverage guarantees under arbitrary hardware noise conditions. Empirical studies on an IBM quantum processor demonstrate that AQCP achieves target coverage levels and exhibits greater stability than quantum conformal prediction.

تقدم دراسة جديدة نموذج التنبؤ التكيفي الكمي (AQCP)، وهو خوارزمية مبتكرة تهدف إلى تحسين موثوقية التنبؤات في التعلم الآلي الكمي من خلال معالجة التحديات التي يطرحها الضجيج المتغير بمرور الوقت في المعالجات الكمية. تعتمد هذه الطريقة على الإطار القائم للتنبؤ الكمي، مما يضمن أن تظل مجموعات التنبؤ صالحة مع مرور الوقت من خلال إعادة المعايرة المتكررة.

Un nuevo estudio presenta la Predicción Conformal Cuántica Adaptativa (AQCP), un algoritmo innovador diseñado para mejorar la fiabilidad de las predicciones en el aprendizaje automático cuántico al abordar los desafíos que plantea el ruido variable en el tiempo de los procesadores cuánticos. Este método se basa en el marco existente de la predicción conformal cuántica, asegurando que los conjuntos de predicciones mantengan su validez a lo largo del tiempo mediante recalibraciones repetidas.

Une nouvelle étude présente la prédiction conforme quantique adaptative (AQCP), un algorithme innovant conçu pour améliorer la fiabilité des prévisions en apprentissage automatique quantique en abordant les défis posés par le bruit variable dans le temps des processeurs quantiques. Cette méthode s'appuie sur le cadre existant de la prédiction conforme quantique, garantissant que les ensembles de prévisions maintiennent leur validité au fil du temps grâce à une recalibration répétée.

A new study introduces Adaptive Quantum Conformal Prediction (AQCP), an innovative algorithm designed to enhance prediction reliability in quantum machine learning by addressing the challenges posed by time-varying noise in quantum processors. This method builds on the existing framework of quantum conformal prediction, ensuring that prediction sets maintain their validity over time through repeated recalibration.

Adaptive Conformal Prediction for Quantum Machine Learning

President Donald Trump’s new “<a href="https://www.whitehouse.gov/presidential-actions/2025/11/launching-the-genesis-mission/">Genesis Mission</a>” unveiled Monday, November 24, 2025, is billed as a generational leap in how the United States does science akin to the Manhattan Project that created the atomic bomb during World War II. The executive order <a href="https://www.reuters.com/business/trump-aims-boost-ai-innovation-build-platform-harness-government-data-2025-11-24/">directs</a> the Department of Energy (DOE) to build a “closed-loop AI experimentation platform” that links the country’s 17 national laboratories, federal supercomputers, and decades of government scientific data into “one cooperative system for research.” The White House fact sheet casts the initiative as a way to “transform how scientific research is conducted” and “accelerate the speed of scientific discovery,” with priorities spanning biotechnology, critical materials, nuclear fission and fusion, quantum information science, and semiconductors. <a href="https://www.energy.gov/articles/energy-department-launches-genesis-mission-transform-american-science-and-innovation">DOE’s own release</a> calls it “the world’s most complex and powerful scientific instrument ever built” and quotes Under Secretary for Science Darío Gil describing it as a “closed-loop system” linking the nation’s most advanced facilities, data, and computing into “an engine for discovery that doubles R&amp;D productivity.”The text of the order outlines mandatory steps DOE must complete within 60, 90, 120, 240, and 270 days—including identifying all Federal and partner compute resources, cataloging datasets and model assets, assessing robotic laboratory infrastructure across national labs, and demonstrating an initial operating capability for at least one scientific challenge within nine months.The <a href="https://genesis.energy.gov/">DOE’s own Genesis Mission website</a> adds important context: the initiative is launching with a broad coalition of private-sector, nonprofit, academic, and utility collaborators. The list spans multiple sectors—from advanced materials to aerospace to cloud computing—and includes participants such as Albemarle, Applied Materials, Collins Aerospace, GE Aerospace, Micron, PMT Critical Metals, and the Tennessee Valley Authority. That breadth signals DOE’s intent to position Genesis not just as an internal research overhaul but as a national industrial effort connected to manufacturing, energy infrastructure, and scientific supply chains.The collaborator list also includes many of the most influential AI and compute firms in the United States: OpenAI for Government, Anthropic, Scale AI, Google, Microsoft, NVIDIA, AWS, IBM, Cerebras, HPE, Hugging Face, and Dell Technologies. The DOE frames Genesis as a national-scale instrument — a single “intelligent network,&quot; an “end-to-end discovery engine,” one intended to generate new classes of high-fidelity data, accelerate experimental cycles, and reduce research timelines from “years to months.” The agency casts the mission as foundational infrastructure for the next era of American science.Taken together, the roster outlines the technical backbone likely to shape the mission’s early development—hardware vendors, hyperscale cloud providers, frontier-model developers, and orchestration-layer companies. DOE does not describe these entities as contractors or beneficiaries, but their inclusion demonstrates that private-sector technical capacity will play a defining role in building and operating the Genesis platform.What the administration has not provided is just as striking: no public cost estimate, no explicit appropriation, and no breakdown of who will pay for what. Major news outlets including <a href="https://www.reuters.com/business/trump-aims-boost-ai-innovation-build-platform-harness-government-data-2025-11-24/">Reuters</a>, <a href="https://apnews.com/article/genesis-mission-trump-ai-25acaea44113c2b60111e8b142344737">Associated Press</a>, <a href="https://www.politico.com/news/2025/11/24/trump-directs-science-agencies-to-embrace-ai-00667318">Politico</a>, and others have all noted that the order “does not specify new spending or a budget request,” or that funding will depend on future appropriations and previously passed legislation. That omission, combined with the initiative’s scope and timing, raises questions not only about how Genesis will be funded and to what extent, but about who it might quietly benefit.<h2>“So is this just a subsidy for big labs or what?”</h2>Soon after DOE promoted the mission on X, <a href="https://x.com/Teknium/status/1993155295099732239?s=20">Teknium</a> of the small U.S. AI lab Nous Research posted a blunt reaction: “So is this just a subsidy for big labs or what.” The line has become a shorthand for a growing concern in the AI community: that the U.S. government could offer some sort of public subsidy for large AI firms facing staggering and rising compute and data costs.That concern is grounded in recent, well-sourced reporting on OpenAI’s finances and infrastructure commitments. <a href="https://techcrunch.com/2025/11/14/leaked-documents-shed-light-into-how-much-openai-pays-microsoft/">Documents</a> obtained and analyzed by tech public relations professional and AI critic <a href="https://www.wheresyoured.at/oai_docs/">Ed Zitron</a> describe a cost structure that has exploded as the company has scaled models like GPT-4, GPT-4.1, and GPT-5.1. <a href="https://www.theregister.com/2025/10/29/microsoft_earnings_q1_26_openai_loss/">The Register </a>has separately inferred from Microsoft quarterly earnings statements that OpenAI lost about $13.5 billion on $4.3 billion in revenue in the first half of 2025 alone. Other outlets and analysts have highlighted projections that show tens of billions in annual losses later this decade if spending and revenue follow current trajectoriesBy contrast, Google DeepMind trained its recent <a href="https://venturebeat.com/ai/google-unveils-gemini-3-claiming-the-lead-in-math-science-multimodal-and">Gemini 3 flagship LLM on the company’s own TPU hardware</a> and in its own data centers, giving it a structural advantage in cost per training run and energy management, as covered in Google’s own technical blogs and subsequent financial reporting. Viewed against that backdrop, an ambitious federal project that promises to integrate “world-class supercomputers and datasets into a unified, closed-loop AI platform” and “power robotic laboratories” sounds, to some observers, like more than a pure science accelerator. It could, depending on how access is structured, also ease the capital bottlenecks facing private frontier-model labs.The aggressive DOE deadlines and the order’s requirement to build a national AI compute-and-experimentation stack amplify those questions: the government is now constructing something strikingly similar to what private labs have been spending billions to build for themselves.The order directs DOE to create standardized agreements governing model sharing, intellectual-property ownership, licensing rules, and commercialization pathways—effectively setting the legal and governance infrastructure needed for private AI companies to plug into the federal platform. While access is not guaranteed and pricing is not specified, the framework for deep public-private integration is now fully established.What the order does not do is guarantee those companies access, spell out subsidized pricing, or earmark public money for their training runs. Any claim that OpenAI, Anthropic, or Google “just got access” to federal supercomputing or national-lab data is, at this point, an interpretation of how the framework could be used, not something the text actually promises.Furthermore, the executive order makes no mention of open-source model development — an omission that stands out in light of<a href="https://venturebeat.com/ai/trump-v-p-pick-j-d-vance-praised-for-comments-seemingly-in-support-of-open-source-ai"> remarks last year from Vice President JD Vance</a>, when, prior to assuming office and while serving as a Senator from Ohio and participating in a hearing, he warned against regulations designed to protect incumbent tech firms and was widely praised by open-source advocates.That silence is notable given Vance’s earlier testimony, which many in the AI community interpreted as support for open-source AI or, at minimum, skepticism of policies that entrench incumbent advantages. Genesis instead sketches a controlled-access ecosystem governed by classification rules, export controls, and federal vetting requirements—far from the open-source model some expected this administration to champion.<h2>Closed-loop discovery and “autonomous scientific agents”</h2>Another viral reaction came from AI influencer Chris (<a href="https://x.com/chatgpt21/status/1993139428655542582">@chatgpt21</a> on X), who wrote in an X post that that OpenAI, Anthropic, and Google have already “got access to petabytes of proprietary data” from national labs, and that DOE labs have been “hoarding experimental data for decades.” The public record supports a narrower claim.The order and fact sheet describe “federal scientific datasets—the world’s largest collection of such datasets, developed over decades of Federal investments” and direct agencies to identify data that can be integrated into the platform “to the extent permitted by law.” DOE’s announcement similarly talks about unleashing “the full power of our National Laboratories, supercomputers, and data resources.” It is true that the national labs hold enormous troves of experimental data. Some of it is already public via the Office of Scientific and Technical Information (OSTI) and other repositories; some is classified or export-controlled; much is under-used because it sits in fragmented formats and systems. But there is no public document so far that states private AI companies have now been granted blanket access to this data, or that DOE characterizes past practice as “hoarding.”What is clear is that the administration wants to unlock more of this data for AI-driven research and to do so in coordination with external partners. Section 5 of the order instructs DOE and the Assistant to the President for Science and Technology to create standardized partnership frameworks, define IP and licensing rules, and set “stringent data access and management processes and cybersecurity standards for non-Federal collaborators accessing datasets, models, and computing environments.”Equally notable is the national-security framing woven throughout the order. Multiple sections invoke classification rules, export controls, supply-chain security, and vetting requirements that place Genesis at the junction of open scientific inquiry and restricted national-security operations. Access to the platform will be mediated through federal security norms rather than open-science principles.<h2>A moonshot with an open question at the center</h2>Taken at face value, the Genesis Mission is an ambitious attempt to use AI and high-performance computing to speed up everything from fusion research to materials discovery and pediatric cancer work, using decades of taxpayer-funded data and instruments that already exist inside the federal system. The executive order spends considerable space on governance: coordination through the National Science and Technology Council, new fellowship programs, and annual reporting on platform status, integration progress, partnerships, and scientific outcomes. The order also codifies, for the first time, the development of AI agents capable of generating hypotheses, designing experiments, interpreting results, and directing robotic laboratories—an explicit embrace of automated scientific discovery and a significant departure from prior U.S. science directives.Yet the initiative also lands at a moment when frontline AI labs are buckling under their own compute bills, when one of them—OpenAI—is reported to be spending more on running models than it earns in revenue, and when investors are openly debating whether the current business model for proprietary frontier AI is sustainable without some form of outside support.In that environment, a federally funded, closed-loop AI discovery platform that centralizes the country’s most powerful supercomputers and data is inevitably going to be read in more than one way. It may become a genuine engine for public science. It may also become a crucial piece of infrastructure for the very companies driving today’s AI arms race.Standing up a platform of this scale—complete with robotic labs, synthetic data generation pipelines, multi-agency datasets, and industrial-grade AI agents—would typically require substantial, dedicated appropriations and a multi-year budget roadmap. Yet the order remains silent on cost, leaving observers to speculate whether the administration will repurpose existing resources, seek congressional appropriations later, or rely heavily on private-sector partnerships to build the platform.For now, one fact is undeniable: the administration has launched a mission it compares to the Manhattan Project without telling the public what it will cost, how the money will flow, or exactly who will be allowed to plug into it. <h2>How enterprise tech leaders should interpret the Genesis Mission</h2>For enterprise teams already building or scaling AI systems, the Genesis Mission signals a shift in how national infrastructure, data governance, and high-performance compute will evolve in the U.S.—and those signals matter even before the government publishes a budget. The initiative outlines a federated, AI-driven scientific ecosystem where supercomputers, datasets, and automated experimentation loops operate as tightly integrated pipelines. That direction mirrors the trajectory many companies are already moving toward: larger models, more experimentation, heavier orchestration, and a growing need for systems that can manage complex workloads with reliability and traceability.Even though Genesis is aimed at science, its architecture hints at what will become expected norms across American industries.The specificity of the order’s deadlines also signals where enterprise expectations may shift next: toward standardized metadata, provenance tracking, multi-cloud interoperability, AI pipeline observability, and rigorous access controls. As DOE operationalizes Genesis, enterprises—particularly in regulated sectors such as biotech, energy, pharmaceuticals, and advanced manufacturing—may find themselves evaluated against emerging federal norms for data governance and AI-system integrity.The lack of cost detail around Genesis does not directly alter enterprise roadmaps, but it does reinforce the broader reality that compute scarcity, escalating cloud costs, and rising standards for AI model governance will remain central challenges. Companies that already struggle with constrained budgets or tight headcount—particularly those responsible for deployment pipelines, data integrity, or AI security—should view Genesis as early confirmation that efficiency, observability, and modular AI infrastructure will remain essential. As the federal government formalizes frameworks for data access, experiment traceability, and AI agent oversight, enterprises may find that future compliance regimes or partnership expectations take cues from these federal standards.Genesis also underscores the growing importance of unifying data sources and ensuring that models can operate across diverse, sometimes sensitive environments. Whether managing pipelines across multiple clouds, fine-tuning models with domain-specific datasets, or securing inference endpoints, enterprise technical leaders will likely see increased pressure to harden systems, standardize interfaces, and invest in complex orchestration that can scale safely. The mission’s emphasis on automation, robotic workflows, and closed-loop model refinement may shape how enterprises structure their internal AI R&amp;D, encouraging them to adopt more repeatable, automated, and governable approaches to experimentation. In this sense, Genesis may serve as an early signal of how national-level AI infrastructure is likely to influence private-sector requirements, especially for companies operating in critical industries or scientific supply chains.Here is what enterprise leaders should be doing now:<ol><li>Expect increased federal involvement in AI infrastructure and data governance. This may indirectly shape cloud availability, interoperability standards, and model-governance expectations.</li><li>Track “closed-loop” AI experimentation models. This may preview future enterprise R&amp;D workflows and reshape how ML teams build automated pipelines.</li><li>Prepare for rising compute costs and consider efficiency strategies. This includes smaller models, retrieval-augmented systems, and mixed-precision training.</li><li>Strengthen AI-specific security practices. Genesis signals that the federal government is escalating expectations for AI system integrity and controlled access.</li><li>Plan for potential public–private interoperability standards. Enterprises that align early may gain a competitive edge in partnerships and procurement.</li></ol>Overall, Genesis does not change day-to-day enterprise AI operations today. But it strongly signals where federal and scientific AI infrastructure is heading—and that direction will inevitably influence the expectations, constraints, and opportunities enterprises face as they scale their own AI capabilities.

أعلن الرئيس دونالد ترامب عن مهمة جينيسيس في 24 نوفمبر 2025، مشبهاً إياها بمشروع مانهاتن، بهدف ثورة البحث العلمي في الولايات المتحدة. توجّه المبادرة وزارة الطاقة لإنشاء منصة تجريبية مغلقة للذكاء الاصطناعي تربط بين المختبرات الوطنية والبيانات الحكومية لتعزيز قدرات البحث.

El presidente Donald Trump anunció la Misión Génesis el 24 de noviembre de 2025, comparándola con el Proyecto Manhattan, con el objetivo de revolucionar la investigación científica en EE. UU. La iniciativa ordena al Departamento de Energía crear una plataforma de experimentación de IA en bucle cerrado que integre laboratorios nacionales y datos gubernamentales para mejorar las capacidades de investigación.

Le président Donald Trump a annoncé la Mission Genesis le 24 novembre 2025, la comparant au projet Manhattan, visant à révolutionner la recherche scientifique aux États-Unis. L'initiative ordonne au Département de l'Énergie de créer une plateforme d'expérimentation IA en boucle fermée qui intègre les laboratoires nationaux et les données gouvernementales pour améliorer les capacités de recherche.

President Donald Trump announced the Genesis Mission on November 24, 2025, likening it to the Manhattan Project, aiming to revolutionize scientific research in the U.S. The initiative directs the Department of Energy to create a closed-loop AI experimentation platform that integrates national laboratories and government data for enhanced research capabilities.

What enterprises should know about The White House's new AI 'Manhattan Project' the Genesis Mission

<img width="800" height="450" src="https://analyticsindiamag.com/wp-content/uploads/2025/11/Using-AI-to-Build-AI-is-Questionable-1300x731.jpg" class="webfeedsFeaturedVisual wp-post-image" alt="" style="display: block; margin: auto; margin-bottom: 5px;max-width: 100%;" link_thumbnail="" decoding="async" loading="lazy" srcset="https://analyticsindiamag.com/wp-content/uploads/2025/11/Using-AI-to-Build-AI-is-Questionable-1300x731.jpg 1300w, https://analyticsindiamag.com/wp-content/uploads/2025/11/Using-AI-to-Build-AI-is-Questionable-768x432.jpg 768w, https://analyticsindiamag.com/wp-content/uploads/2025/11/Using-AI-to-Build-AI-is-Questionable-1536x864.jpg 1536w, https://analyticsindiamag.com/wp-content/uploads/2025/11/Using-AI-to-Build-AI-is-Questionable.jpg 1600w" sizes="auto, (max-width: 800px) 100vw, 800px" /> A key component of enabling AI-powered GCCs is talent upskilling.
The post <a href="https://analyticsindiamag.com/gcc/how-meltwaters-hyderabad-hub-is-building-ai-driven-media-intelligence-2/">AI-Driven GCCs’ Race Gets Dense as Indian Firms Follow IBM</a> appeared first on <a href="https://analyticsindiamag.com">Analytics India Magazine</a>.

تقوم الشركات الهندية بتكثيف جهودها لإنشاء مراكز القدرات العالمية المدفوعة بالذكاء الاصطناعي، متبعةً مثال شركة IBM في استغلال الذكاء الاصطناعي لتعزيز الكفاءة التشغيلية والابتكار. يتم التأكيد على هذا التحول من خلال التركيز على تطوير المهارات كعنصر حاسم للنجاح في هذا المشهد التنافسي.

Las empresas indias están intensificando sus esfuerzos para establecer Centros de Capacidades Globales (GCC) impulsados por IA, siguiendo el ejemplo de IBM en el aprovechamiento de la inteligencia artificial para mejorar la eficiencia operativa y la innovación. Este cambio se subraya con un enfoque en la mejora de habilidades como un componente crítico para el éxito en este paisaje competitivo.

Les entreprises indiennes intensifient leurs efforts pour établir des centres de capacité mondiale (GCC) axés sur l'IA, suivant l'exemple d'IBM dans l'utilisation de l'intelligence artificielle pour améliorer l'efficacité opérationnelle et l'innovation. Ce changement est souligné par un accent sur le perfectionnement des talents comme élément clé du succès dans ce paysage concurrentiel.

Indian firms are intensifying their efforts to establish AI-driven Global Capability Centers (GCCs), following IBM's lead in leveraging artificial intelligence to enhance operational efficiency and innovation. This shift is underscored by a focus on talent upskilling as a critical component for success in this competitive landscape.

Adaptive Conformal Prediction for Quantum Machine Learning

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