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MicroCloud Hologram Inc. Researches CV-QNN (Continuous Variable Quantum Neural Networks) Technology and Builds Variational Quantum Circuits Embedded in CV Architecture

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SHENZHEN, China, March 17, 2025 /PRNewswire/ — MicroCloud Hologram Inc. (NASDAQ: HOLO), (“HOLO” or the “Company”), a technology service provider, they are researching CV-QNN (Continuous Variable Quantum Neural Networks) technology, with the aim of building Variational Quantum Circuits embedded in CV architecture. In this way, it is possible not only to quantumize classical neural networks, but also to design various specialized quantum models, such as convolutional quantum networks, recursive quantum networks, and residual quantum networks, providing new tools for quantum artificial intelligence technology.

The core of HOLO CV-QNN lies in achieving affine transformations and nonlinear mappings in neural networks through layered continuously parameterized quantum gates and nonlinear activation functions. The CV architecture is a form of quantum computing where information is encoded using continuous degrees of freedom, such as the amplitude and phase of electromagnetic fields. This contrasts with the DV architecture, which uses discrete quantum bits. The CV architecture is more closely aligned with classical information processing methods, thus offering inherent advantages when implementing neural networks. The basic operational units in CV architecture are Gaussian and non-Gaussian transformations of quantum states.

Affine transformations are fundamental operations in neural networks, typically composed of linear transformations (matrix multiplication) and bias terms (vector addition). In CV-QNN, affine transformations are realized through Gaussian gates. Gaussian gates are operations that preserve the Gaussian distribution of quantum states, including squeezing, displacement, and rotation gates. These gates can precisely control the amplitude and phase of quantum states, thereby simulating the linear operations in classical neural networks.

Nonlinear activation functions are key to enabling neural networks to represent complex features. In classical neural networks, common activation functions include ReLU, Sigmoid, and Tanh, among others. In the CV architecture, nonlinearity is achieved through non-Gaussian gates, such as polarized optical nonlinear operations or non-Gaussian optical crystals. The nonlinearity introduced by these non-Gaussian gates enables CV-QNN to represent more complex functions, enhancing the model’s expressiveness.

HOLOCV-QNN adopts a layered structure, with each layer composed of several continuously parameterized quantum gates. This layered design is similar to the multilayer perceptron structure in classical neural networks, allowing CV-QNN to perform complex nonlinear transformations while preserving quantum coherence. Additionally, this layered structure is theoretically universal, meaning that through appropriate combinations of gate operations, it can approximate any continuous function.

HOLO CV-QNN leverages quantum superposition and entanglement properties, offering the potential for exponential speedup when processing large-scale data. Additionally, because the information encoding in the CV architecture is closer to classical computing methods, CV-QNN boasts strong scalability and can seamlessly interface with existing classical computing systems. Moreover, the design of CV-QNN fully exploits the energy efficiency advantages of continuous-variable quantum computing. By using Gaussian and non-Gaussian gates, complex quantum operations can be achieved at a lower resource cost, providing a practical and feasible solution during the stage when quantum computer hardware is not yet fully developed.

The potential applications of CV-QNN are vast. It can achieve more efficient image classification, object detection, and semantic segmentation through quantum convolutional networks; enhance performance in text generation, sentiment analysis, and machine translation using quantum recursive networks; offer faster solutions in quantum chemistry, materials science, and complex system simulations; and enable more accurate market forecasting and risk assessment through quantum neural networks.

The emergence of HOLO Continuous Variable Quantum Neural Networks (CV-QNN) offers a fresh perspective on the integration of quantum computing and artificial intelligence. By embedding the structure and functions of classical neural networks within the framework of quantum computing, this technology not only significantly enhances the computational efficiency of models but also expands their application boundaries across different fields. From quantum convolutional networks to recursive quantum networks and residual quantum networks, CV-QNN technology demonstrates its potential in multiple scenarios, including image processing, natural language processing, and scientific computing. These advances signify that we are gradually entering an era driven by quantum artificial intelligence.

However, the HOLO CV-QNN technology still faces several challenges. For example, issues such as the stability of quantum hardware and the optimization of computational resources need further attention. Additionally, the potential accumulation of errors during the training process of quantum networks and the need for more efficient designs of quantum optimization algorithms present new challenges for both academia and industry. Nonetheless, these challenges also represent opportunities. As quantum hardware advances and software tools improve, the performance of CV-QNN will continue to enhance, and its future application scenarios will become even more widespread.

Against the backdrop of quantum technology gradually transforming the world, HOLO CV-QNN not only represents a new computational tool but also holds the potential to redefine the boundaries of artificial intelligence capabilities. It is believed that as this technology continues to develop, it will become the core driving force behind the next generation of intelligent systems. Whether it is unveiling the mysteries of nature in scientific research or solving complex practical problems in industry, the potential of CV-QNN technology will be exponentially magnified, bringing unprecedented opportunities.

About MicroCloud Hologram Inc.

MicroCloud is committed to providing leading holographic technology services to its customers worldwide. MicroCloud’s holographic technology services include high-precision holographic light detection and ranging (“LiDAR”) solutions, based on holographic technology, exclusive holographic LiDAR point cloud algorithms architecture design, breakthrough technical holographic imaging solutions, holographic LiDAR sensor chip design and holographic vehicle intelligent vision technology to service customers that provide reliable holographic advanced driver assistance systems (“ADAS”). MicroCloud also provides holographic digital twin technology services for customers and has built a proprietary holographic digital twin technology resource library. MicroCloud’s holographic digital twin technology resource library captures shapes and objects in 3D holographic form by utilizing a combination of MicroCloud’s holographic digital twin software, digital content, spatial data-driven data science, holographic digital cloud algorithm, and holographic 3D capture technology. For more information, please visit http://ir.mcholo.com/ 

Safe Harbor Statement

This press release contains forward-looking statements as defined by the Private Securities Litigation Reform Act of 1995. Forward-looking statements include statements concerning plans, objectives, goals, strategies, future events or performance, and underlying assumptions and other statements that are other than statements of historical facts. When the Company uses words such as “may,” “will,” “intend,” “should,” “believe,” “expect,” “anticipate,” “project,” “estimate,” or similar expressions that do not relate solely to historical matters, it is making forward-looking statements. Forward-looking statements are not guarantees of future performance and involve risks and uncertainties that may cause the actual results to differ materially from the Company’s expectations discussed in the forward-looking statements. These statements are subject to uncertainties and risks including, but not limited to, the following: the Company’s goals and strategies; the Company’s future business development; product and service demand and acceptance; changes in technology; economic conditions; reputation and brand; the impact of competition and pricing; government regulations; fluctuations in general economic; financial condition and results of operations; the expected growth of the holographic industry and business conditions in China and the international markets the Company plans to serve and assumptions underlying or related to any of the foregoing and other risks contained in reports filed by the Company with the Securities and Exchange Commission (“SEC”), including the Company’s most recently filed Annual Report on Form 10-K and current report on Form 6-K and its subsequent filings. For these reasons, among others, investors are cautioned not to place undue reliance upon any forward-looking statements in this press release. Additional factors are discussed in the Company’s filings with the SEC, which are available for review at www.sec.gov. The Company undertakes no obligation to publicly revise these forward-looking statements to reflect events or circumstances that arise after the date hereof.

 

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Annexus Health Unifies Patient Financial Assistance with Connected, End-to-End Platform

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Built on AssistPoint®, the Annexus Platform connects patient assessment and enrollment, claims management, and ongoing funding management to help patients start treatment sooner and enable healthcare providers to improve reimbursement performance

CRANBERRY TOWNSHIP, Pa., Nov. 10, 2026 /PRNewswire/ — Annexus Health today announced the launch of the Annexus Platform, designed to help healthcare providers identify, secure, and manage patient financial assistance throughout treatment. Built on the proven foundation of the AssistPoint software, the Platform uses AI and automation within existing provider workflows to match patients with assistance opportunities, enroll patients in financial assistance programs, manage claims, and maintain funding across the course of care.

Provider organizations can manage the Platform using their own teams, share workflow execution with Annexus, or outsource financial assistance operations through Annexus Managed Services. The Platform works alongside existing provider systems and requires no upfront capital investment.

Financial toxicity, or the financial distress caused by medical costs, can put needed treatment out of reach, especially in specialty care. Even with health insurance, patients with cancer can face out-of-pocket costs that disrupt access to care, according to the National Cancer Institute.

Financial assistance can help, but provider teams often manage program requirements, applications, claims, and renewals through a patchwork of spreadsheets, manual processes, and separate program portals. That fragmented administrative work leaves less time to help patients understand their options and secure support before costs become a barrier to care. Assistance efforts can also be reactive, beginning only when a patient asks for help. As a result, eligible patients can be overlooked and available funding can go unused. Delays in assistance can affect treatment starts and adherence, while unresolved claims can delay provider reimbursement.

Founded in 2017, Annexus pioneered software-as-a-service technology to connect provider workflows with financial assistance programs. As its network expanded beyond community oncology into infusion centers, specialty pharmacies, home infusion, and health systems, the company added operational services to support provider needs. The Platform and Annexus Managed Services build on that experience to help organizations manage the full financial assistance lifecycle at scale.

“Since partnering with Annexus Health, we’ve seen a significant evolution in how we support patients and manage financial assistance,” said Dr. Jeff Vacirca, CEO of New York Cancer & Blood Specialists. “The Annexus Platform helps us connect patients to manufacturer co-pay assistance and foundation funding, and with Annexus Managed Services, we’ve been able to extend that impact even further by strengthening our revenue cycle processes and achieving outcomes we hadn’t thought possible.”

Three Connected Workflows
The Platform brings treatment orders, assistance applications, claims, and funding information together across provider systems, including electronic health record, practice management, billing, infusion, and pharmacy systems. This connection supports three core workflows:

Initial Patient Assessment: Automatically screens every treatment order and uses AI to help match patients with thousands of financial assistance opportunities, including all manufacturer co-pay programs and charitable foundation programs across high-cost specialty therapeutics. The Platform advances likely matches through an organized enrollment processing workflow, helping teams manage applications and track status and outcomes.Claims Management: Uses automation and AI-supported matching to review medical benefit claims from provider systems and identify those that may qualify for reimbursement through assistance programs. The Platform then helps teams prepare, submit, track, and resolve eligible claims, with final documentation returned to the provider’s billing system.Ongoing Patient Management: Monitors assistance throughout treatment, including award balances and expiration dates. It alerts teams when funds run low, keeps assistance information accurate through reconciliation, maintains a watchlist for patients awaiting available funding, and supports re-enrollment.

For providers, these workflows help teams identify assistance earlier, reduce administrative work, and maintain funding throughout treatment while helping improve financial assistance collections and reimbursement performance.

Connecting Life Sciences Programs with Provider Workflows
For life sciences organizations, the Platform extends patient support programs into provider workflows where financial assistance is managed and works alongside existing hub and patient access infrastructure. When connectivity with a program is enabled, relevant enrollment, claims, and ongoing funding activity can be exchanged with the Platform, helping keep information synchronized across systems. Annexus’ dedicated life sciences team works with manufacturers to extend that connectivity across their brand portfolios.

“Patients should not have to know which assistance programs exist or when to ask for help,” said Brad Frazier, CEO and co-founder of Annexus Health. “Our goal is to help providers identify that support earlier and manage it throughout treatment. By connecting assessment, enrollment, claims management, and ongoing funding management, the Platform takes on much of the administrative work, giving teams more time to focus on and interact with patients. That can help with faster treatment starts and reduced financial toxicity while improving reimbursement performance for providers.”

Real-World Results Across the Annexus Network
Annexus has helped secure more than $11 billion in financial assistance for more than 400,000 patients. Its provider network spans more than 735 healthcare organizations, 32,000 sites of care, and 124,000 treating providers. Measured results include:

5.2-day reduction in time to treatment, on average75% increase in financial assistance collections, on average*55% lower cost per dollar of financial assistance collected, on average*78% reduction in days to payment on digitally integrated claims, from 19.5 days to 4.3 days

*Based on pre- and post-implementation comparisons among representative provider clients.

Annexus maintains HITRUST r2 Certification, an independent validation of its information security program.

Annexus will showcase the Platform at booth P10 during McKesson Accelerate, Nov. 16-18, at Wynn Las Vegas. For more information, visit annexushealth.com.

About Annexus Health
Annexus Health is a healthcare technology company providing the connected infrastructure behind patient financial assistance. Through automation, intelligence, and operational expertise, Annexus helps providers and life sciences organizations reduce complexity, improve organizational performance, and connect more eligible patients to available financial support throughout the patient journey.

Media contact:
AnnexusPR@supremecomms.ai

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Typeform Brings Form Creation, Customer Insights, and Automated Workflows to ChatGPT and Claude

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New capability lets teams create forms and landing pages, analyze responses, and automate next steps within the AI tools they already use

SAN FRANCISCO, Nov. 10, 2026 /PRNewswire/ —Typeform, the AI research platform that turns conversations into qualitative and quantitative insights, today announced that it is now available directly within ChatGPT, Claude, and other MCP-compatible tools through a Model Context Protocol (MCP) server, giving businesses the power to use Typeform where and how they work.

AI tools are increasingly becoming a starting point for work, but the customer conversations and feedback that inform that work often live across separate applications and workflows. Many teams still move between platforms to manually transfer vital context, adding steps that prevent them from acting quickly. Typeform’s MCP server helps close that gap by allowing users to access Typeform capabilities from AI tools. Instead of navigating across tools to build a form or landing page or configure follow-up, users can describe what they want to accomplish in natural language, and those tasks will be completed directly in Typeform.

“AI makes it easier to act, but better decisions still depend on understanding your customers,” said Aleks Bass, Chief Product and Technology Officer at Typeform. “We’re bringing customer feedback into the tools where teams are already working, so they can ask a question, understand what they hear, and take the next step while that insight is still relevant.”

With Typeform’s MCP server, teams can do the following directly from ChatGPT, Claude, or other AI assistants, without switching to Typeform:

Create customer conversations faster: Describe the form, landing page, or study and use AI to create, edit, and publish it in Typeform.Turn feedback into insight: Ask questions about responses, identify recurring themes, and summarize what customers are saying without manually exporting the data for analysis.Work with customer context: Create and update contact profiles, ensuring the most recently collected data is synced.Act on what customers say: Build and edit automations for follow-up messages and next steps based on customer responses.

For example, a marketer could ask an AI assistant to create a lead-generation form, organize the contacts it generates, and set up follow-up. A customer experience team could use an assistant to analyze feedback responses and surface recurring themes, all without leaving the ChatGPT or Claude interface.

The connection also works in the other direction. With MCP connectors in Typeform AI, teams can bring business context from platforms like Canva, Notion, Slack, Shopify Storefront, and Stripe into Typeform to create more relevant experiences informed by existing customer information. A product team could turn a Slack thread into a customer feedback form, while an e-commerce team could use its Shopify product catalog to build a personalized product recommendation quiz.

This launch is part of Typeform’s broader investment in AI-powered customer understanding. Earlier this year, Typeform launched Research Flow, its AI-moderated research platform that combines quantitative scale with qualitative depth to help teams uncover not only what customers say, but why. Looking ahead to early 2027, Typeform plans to extend these same MCP-powered capabilities to Research Flow, enabling teams to bring AI-moderated research into the tools and workflows they already use.

Typeform’s MCP server is available on all Typeform plans. Users can connect Typeform directly to ChatGPT, Claude, or Cursor to get started. For more information, including how to connect other MCP-compatible AI tools, visit typeform.com/mcp.

About Typeform
Typeform helps businesses turn conversations into insight and action. Its platform brings together human-first forms, AI-moderated research that combines qualitative depth with quantitative scale, and AI-powered workflows to help teams engage customers, understand them more deeply, and make better decisions. Typeform serves more than 150,000 customers and has been used by 95% of the Fortune 500. For more information, visit typeform.com.

 

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SOURCE Typeform S.L.

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Rescale Expands Agentic Digital Engineering with Customizable AI Agents for R&D Teams

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Kairos Power and Oak Ridge National Laboratory use agentic workflows on the Rescale platform for advanced nuclear reactor design and manufacturing

SAN FRANCISCO, Nov. 10, 2026 /PRNewswire/ — Rescale, the digital engineering platform for the AI era, today introduced new agentic digital engineering capabilities that allow R&D and engineering teams across advanced industries to customize AI agents to their exacting standards, bringing agentic AI into engineering work where every result must be reviewable and traceable.

As engineering organizations continue to deploy more agents into production, the work is shifting from automating individual tasks to running end-to-end engineering processes. That shift requires an engineering-specific agentic harness: the structure and context that govern how agents operate, ensure they meet specific standards, and keep humans in the loop. 

New Agentic Projects on the Rescale platform provide the framework and structure, giving engineers a way to customize agents and build reusable agentic workflows. Teams can define each project with their own context, skills, tools, and guardrails, share projects across teams, and retain approved learnings through project memory. Expanded engineering intelligence capabilities supply the underlying context by connecting data across requirements, design, simulation, test, and process systems into foundational knowledge graphs. As agents execute workflows, the platform generates project-specific work graphs that provide traceability and capture critical learnings. Those learnings enrich agent context, build domain expertise, preserve knowledge, and drive process improvements that make engineering work more efficient and scalable. 

Rescale is also introducing Rescale Desktop, which extends Rescale directly to engineers’ local machines, along with expanded Model Context Protocol (MCP) connectors for interoperability across Rescale’s leading ecosystem of engineering applications and complementary agentic tools, including Rescale MCP availability on the Claude and Codex directories.

Rescale Edison, the platform’s AI assistant and agent orchestrator, now includes persistent memory so engineers can work faster by describing their intent in natural language. Together, these new capabilities work to provide an engineering-specific harness that ensures agents execute to the same standards as experienced engineers, helping organizations develop the trust in agentic outcomes needed to transform operations.

“Our customers build aircraft, vehicles, and reactors, so the agents supporting that work have to deliver reliable results. Agentic Projects provide the needed context that grows with every iteration. This is a material advancement toward an engineering harness built for how these teams actually work,” said Jacob Surber, vice president of product at Rescale.

Kairos Power and the Department of Energy’s Manufacturing Demonstration Facility (MDF) at Oak Ridge National Laboratory (ORNL), with support from Rescale AI developer engineers, have developed a digital agentic scaffolding on the Rescale platform as part of a collaboration to test advanced manufacturing and construction methods for Hermes 2, Kairos Power’s 50-megawatt fluoride-salt-cooled reactor. Working in a shared environment, agents have exercised thermal, flow, and shielding simulation and reporting workflows for the reactor cavity and bioshield plug designs, with engineers approving each agent action. Kairos Power projects that agentic workflows built from past simulations could increase end-to-end simulation analysis efficiency by up to 3x while preserving the data traceability required for regulatory compliance.

“Kairos Power depends on quick learning cycles: design it, build it, test it, and carry what we learn into the next round,” said Ed Blandford, chief technology officer and co-founder of Kairos Power. “Much of the difficulty in scaling nuclear energy comes down to manufacturing, from the smallest components to large structures. Rescale brings agentic engineering into the simulations that support that work, connecting our engineers with our ORNL collaborators in one environment built for context and traceability. With agents managing setup, execution, and reporting, our people can spend their time on the engineering judgment and rigor the work demands.”

ORNL is encoding its simulation and additive manufacturing expertise in agents on the platform so that engineers can adopt advanced methods to address industry challenges. 

“The Manufacturing Demonstration Facility works because industry engineers and lab researchers solve problems side by side,” said Ryan Dehoff, MDF director. “Rescale provides a digital environment for that collaboration. Nuclear innovation demands speed and rigor at the same time, and you can’t trade one for the other. Agents allow ORNL’s expertise to scale beyond our facility.”

The launch also highlights how agentic digital engineering works with Rescale’s AI physics solution to make surrogate models easier to build and more accessible to engineering teams. A new dataset creation agent automates the process of turning simulation data into training-ready datasets, reducing the specialized machine learning work involved. This is one of many new AI physics capabilities that help companies put valuable surrogate models into the hands of more engineers:

Auto Tune and guided pre-processing automate and simplify surrogate model training so organizations can develop better models with fewer specialized resources. Uncertainty quantification builds trust that engineers are using an appropriate surrogate model and flags predictions that should be confirmed with a full-fidelity simulation.Design explorer is a new local interface that allows design engineers to use surrogate models to evaluate thousands of design options from their local environment.

For high performance computing (HPC) administrators, new cluster performance metrics show where execution slows and why jobs are waiting. A new priority score considers wait time, job size, and resource usage to help teams allocate resources fairly.

These new and enhanced capabilities across agentic digital engineering, AI physics, and intelligent HPC work together to deliver compounding value for R&D and engineering organizations as they adopt AI-first operations across every stage of their product development lifecycles.

Organizations can learn more about the new capabilities at rescale.com.

About Rescale

Rescale is the digital engineering platform for the AI era. The R&D and engineering teams shaping the future across aerospace, automotive, defense, energy, public sector, life sciences, and manufacturing trust the Rescale platform to accelerate innovation. Rescale integrates agentic digital engineering, AI physics, advanced modeling and simulation, and intelligent HPC solutions on a single platform to unify product data, automate workflows, optimize resources, and bring breakthroughs to market faster.

Rescale is backed by leading investors such as NVIDIA, Sam Altman, Jeff Bezos, Paul Graham, Peter Thiel, Microsoft, Hitachi, and University of Michigan.

For more information, visit rescale.com.

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SOURCE Rescale Inc.

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