Quantum Surge AI Value Network Ecosystem Project White Paper

1st CONTENTS Abstract Chapter 1 Project Background 1.1 Industry Status ................................................................................................... 3rd 1.2 Core Challenges ................................................................................................5th Chapter 2 Project Introduction ................................................ 错误！未定义书签。 2.1 Project Origin ..................................................................................................... 7th 2.2 Project Positioning and Mission ...................................................................... 8th 2.3 Core Innovations ............................................................................................... 9th Chapter 3 Project Ecosystem .................................................. 错误！未定义书签。 3.1 Layered Subnet Architecture ......................................................................... 10th 3.2 Ecosystem Participant Roles ........................................................................ 12th 3.3 AI + Web3 Synergistic Use Cases ............................................................... 13th Chapter 4 Core Technical Architecture ................................. 错误！未定义书签。 4.1 Overall Architecture Design ........................................................................... 15th 4.2 Key Technical Solutions ................................................................................. 16th 4.3 Dedicated Wallet & Toolchain ....................................................................... 17th Chapter 5 Ecosystem Governance Mechanism .................. 错误！未定义书签。 5.1 Decentralized Autonomous ........................................................................... 19th 5.2 Voting and Proposal Mechanism .................................................................. 20th Chapter 6 Economic System ............................................................ 错误！未定义书签。 6.1 Token Issuance Mechanism ......................................................................... 22nd 6.2 Token Utility ...................................................................................................... 23rd Chapter 7 Core Team .................................................................. 错误！未定义书签。 Chapter 8 Project Development Roadmap ................................................... 27th Chapter 9 Legal and compliance instrucfions 9.1 Compliance Statement ................................................................................ 28th 9.2 Risk Disclosures .............................................................................................. 29th Chapter 10 Conclusion .......................................................... 错误！未定义书签。

2nd Abstract Quantum Surge is an AI value co-creation and distribution platform based on a decentralized network architecture. It innovatively constructs an open value network in which AI computing power, models, and data operate collaboratively. In the current wave of rapid development of the AI industry, the industry is facing core pain points such as uneven distribution of computing power, high model training costs, frequent data privacy leaks, and an unbalanced value distribution system. Quantum Surge was created to solve these industry dilemmas. Quantum Surge employs a layered subnet architecture and an AI consensus incentive mechanism, designing its native token QTS (QTS) as the core carrier for value transfer within the ecosystem. Quantum Surge's core technology system encompasses multi-dimensional AI subnet collaboration protocols, AI effect quantification and verification algorithms, privacy computing, and on-chain rights confirmation integration solutions, enabling efficient scheduling, secure transfer, and fair profit sharing of AI resources. Quantum Surge's ultimate ecosystem vision is to create a world-leading distributed AI resource collaboration network, break down the centralized barriers of the traditional AI industry, promote the democratization and value reconstruction of AI capabilities, and enable ecosystem participants such as computing power providers, data contributors, model developers, and application service providers to obtain reasonable value returns through open collaboration, providing a brand-new underlying infrastructure for the sustainable development of the AI industry.

3rd 1.1 Industry Status In recent years, the global AI industry has experienced explosive growth. According to the AI Empowering All Industries White Paper (2025 Edition) jointly released by Frost & Sullivan and Head Leopard Research Institute, the global AI sector has entered a stage characterized by "strong governance + rapid expansion." In 2024, the global AI market reached $615.7 billion and is projected to surpass $26 trillion by 2030. China and the United States have formed a bipolar dominance: China leads the world with 1,509 large models, while the U.S. maintains its advantage through technological depth and capital strength, with the performance gap between mainstream models in the two countries narrowing to within 4%. In USA, the "AI+" initiative has driven the core industry scale to exceed ¥700 billion in 2024. The eight national hub nodes of the "East Data, West Computing" project account for 70% of the country’s total computing power, laying a solid infrastructure foundation for industrial implementation. Key growth areas include large model development, AI computing power services, and multimodal data processing. With surging demand from downstream applications such as generative AI, autonomous driving, and biopharmaceuticals, AI computing power demand is expanding exponentially. In 2024 alone, global AI computing demand tripled compared to the previous year, leading to severe shortages of high-end computing hardware such as GPUs and specialized AI chips. Simultaneously, the iteration speed of AI models continues to accelerate. From GPT-3.5 to GPT-4o, model parameters have scaled from hundreds of billions

4th to trillions, with the cost of training a single large model reaching tens of millions of dollars. Validating and optimizing model performance also requires massive data support. However, the current resource allocation model in the AI industry exhibits significant centralization. Leading tech companies, leveraging capital and technological advantages, monopolize most high-end computing power, high-quality data, and top-tier models. Small and medium-sized developers and research institutions face challenges such as unaffordable computing power, inaccessible models, and unobtainable data. The integration of decentralized technologies with the AI industry offers a novel approach to break through this centralized dilemma. By adopting a distributed collaboration model to integrate fragmented AI resources and establishing a contribution-based value distribution system, resource monopolies can be effectively dismantled, enabling broader access to AI capabilities. Quantum Surge is built upon this industry trend, creating a decentralized collaboration and value network tailored to the specific needs of the AI industry.

5th 1.2 Core Challenges 1.2.1 Computing Power Layer Centralized computing clusters have led to industry monopolies, with leading enterprises controlling over 70% of global high-end AI computing resources. The cost of accessing computing power for small and medium-sized developers is prohibitively high—for instance, renting a single A100 GPU can exceed $140 per day. Additionally, issues such as inflexible computing power scheduling and poor service stability persist. Furthermore, the uneven geographical distribution of computing resources, compounded by infrastructure limitations in certain regions, exacerbates the computing power divide. 1.2.2 Model Layer High-quality AI models are increasingly closed off, with flagship models from leading companies often available only through paid subscriptions or closed API access, making cross-model collaboration and integration extremely difficult. Moreover, there is a lack of transparent mechanisms for validating model performance. Discrepancies often exist between self-reported performance metrics from model providers and actual application performance, making it challenging for developers to accurately assess model suitability. Additionally, the value generated from model iterations is difficult to attribute, leaving developers' innovative contributions inadequately rewarded. 1.2.3 Data Layer The conflict between data privacy protection and efficient data utilization is becoming increasingly pronounced. On one hand, stringent data security regulations worldwide restrict data flow, discouraging businesses and individuals from sharing data. On the other hand, AI model training requires massive amounts of high-quality data, leading to a growing supply-demand gap. Simultaneously, data contributors lack adequate rights protection. Vast amounts of user behavior and creative data are harvested by platforms for model training without compensation, resulting in a severe imbalance in data value distribution. 1.2.4 Incentive Layer The value distribution system within the AI industry chain has significant

6th shortcomings. Computing power providers only receive basic hardware rental fees and cannot share in the added value from model deployment and application. Data contributors' efforts are often exploited without compensation. Model developers' earnings are disconnected from the actual application value of their models. Meanwhile, platform operators capture most of the profits through resource integration advantages. This imbalanced incentive mechanism severely dampens the enthusiasm of ecosystem participants and stifles innovation within the industry.

7th 2.1 Project Origin With the deep integration of Web3 and AI technologies, decentralized AI has emerged as a new industry trend. On one hand, blockchain’s characteristics—decentralization, immutability, and smart contracts—provide a trusted value carrier for AI resource circulation. On the other hand, breakthroughs in AI technology offer new possibilities for optimizing blockchain network performance and expanding functionality. Seizing this industry opportunity, Quantum Surge was launched. The project team has extensive expertise in the intersection of blockchain and AI. Through research on distributed digital resource collaboration models, they identified that while traditional decentralized network architectures enable basic digital goods collaboration, they fall short in meeting the specific needs of the AI industry. Examples include the lack of mechanisms for quantifying AI model performance, insufficient consideration for data privacy protection, and weak cross-domain AI resource coordination and scheduling capabilities. To address these gaps, the Quantum Surge team proposed an innovative upgrade path, constructing a specialized collaboration system tailored for multi-dimensional AI tasks—including computing power, models, data, and inference—and building a decentralized value network better aligned with the AI industry's requirements.

8th 2.2 Project Positioning and Mission 2.2.1 Project Positioning Quantum Surge is an underlying infrastructure for distributed AI resource collaboration and value distribution. It does not directly provide AI application services but rather establishes an open, trusted, and efficient collaboration platform for global AI developers, computing power providers, data contributors, and application service providers. Through a layered subnet architecture, the Quantum Surge platform enables precise matching and scheduling of AI resources. Smart contracts automate value distribution, while privacy computing and on-chain asset verification technologies safeguard the rights and data security of ecosystem participants. 2.2.2 Project Mission Quantum Surge aims to break down resource barriers and value monopolies in the AI industry, promoting the democratization of AI capabilities and value co-creation. It strives to ensure that every ecosystem participant can fairly access AI resources and that every contribution receives reasonable value in return. Ultimately, Quantum Surge seeks to build a decentralized, self-driven, and sustainable AI industry ecosystem, offering a new collaboration paradigm for global AI technological innovation and industrial implementation.

9th 2.3 Core Innovations 2.3.1 Multi-Dimensional AI Subnet Architecture To address specialized AI tasks, Quantum Surge has built four core subnets: Computing Power Subnet, Model Subnet, Data Subnet, and Inference Service Subnet. The platform also supports subnet creators in launching vertical AI scenario subnets (e.g., Biopharmaceutical AI Subnet, Autonomous Driving AI Subnet). Each subnet operates independently while enabling cross-subnet collaboration, achieving refined management and efficient scheduling of AI resources. 2.3.2 AI Task Consensus Verification Mechanism Quantum Surge innovatively designs a dual value assessment system combining model performance and resource contribution. Building upon traditional Proof of Work (PoW), the system introduces quantifiable AI model performance metrics. Validators not only verify nodes' resource contributions but also evaluate model performance—such as inference accuracy and response speed—using standardized test datasets. This ensures that rewards are allocated to nodes demonstrating "high contribution and high quality," thereby enhancing the overall service level of the ecosystem. 2.3.3 Privacy Computing + On-Chain Asset Verification Quantum Surge employs privacy computing technologies such as federated learning and homomorphic encryption, enabling data to participate in model training without exposing raw information. This approach meets data privacy protection requirements while enabling efficient data utilization. Simultaneously, NFT technology is used for on-chain verification of AI models, data outputs, and computing services, clarifying ownership and usage rights of resources. This protects participants' intellectual property and profit rights, facilitating the value-based circulation of AI assets.

10th 3.1 Layered Subnet Architecture 3.1.1 Computing Power Subnet The Computing Power Subnet serves as the foundational support network within the Quantum Surge ecosystem, responsible for onboarding, scheduling, and verifying distributed AI computing nodes. Computing miners can connect various hardware resources, including GPUs, CPUs, and dedicated AI chips. The platform employs intelligent scheduling algorithms to allocate AI training and inference tasks to the most optimal computing nodes. Furthermore, the subnet integrates a real-time performance monitoring module that verifies the quality of computational output from each node, preventing fraud and ensuring the efficient completion of tasks. 3.1.2 Model Subnet The Model Subnet constitutes the core competency network of the Quantum Surge ecosystem, supporting the deployment, invocation, and performance evaluation of both open-source and proprietary AI models. Model miners can deploy their proprietary or optimized open-source models within the subnet, providing inference and training services to the ecosystem via standardized API interfaces. A model performance leaderboard is maintained within the subnet, updated in real-time based on validator assessments. This facilitates rapid identification of high-quality models for developers and provides model miners with a precise channel to showcase their capabilities.

11th 3.1.3 Data Subnet The Data Subnet functions as the resource provisioning network within the Quantum Surge ecosystem, specializing in privacy-preserving data sharing, labeling, and value monetization. Data contributors can upload anonymized raw data or labeled datasets, participating in model training through federated learning protocols to earn QTS token rewards. The subnet incorporates a data quality assessment mechanism where validators verify the accuracy and completeness of submitted data, ensuring only high-quality, compliant data enters the Quantum Surge ecosystem. 3.1.4 Inference Service Subnet The Inference Service Subnet acts as the application deployment network within the Quantum Surge ecosystem, managing the distribution, execution, and verification of AI inference tasks. Various AI application service providers can submit inference tasks to this subnet. Based on specific computational requirements and latency needs, the subnet automatically matches tasks with the optimal combination of computing power and model nodes to deliver inference services. Additionally, inference results are immutably recorded on-chain, ensuring traceability and service trustworthiness.

12th 3.2 Ecosystem Participant Roles 3.2.1 Computing Miners As providers of computational resources, Computing Miners connect various hardware to compete for tasks within the Computing Power Subnet. They earn QTS token rewards based on the amount of computational power contributed and service stability. Miners can also enhance their node priority by staking tokens, thereby increasing their chances of task allocation. 3.2.2 Model Miners Model Miners are responsible for deploying and maintaining AI models within the Model Subnet, offering services such as inference, training, and model optimization to the ecosystem. Their earnings are directly linked to the model's invocation volume and performance score, with high-quality models receiving more service requests and a larger share of rewards. 3.2.3 Data Contributors Data Contributors supply compliant data or labeling services to the Data Subnet. By participating in model training via privacy-preserving computation protocols, they earn token rewards commensurate with data quality and contribution level. Contributors can retain partial usage rights to their data through on-chain asset verification, enabling long-term value monetization. 3.2.4 Validators Validators act as the "supervisors" of the ecosystem, distributed across various subnets. They are responsible for assessing the service quality of nodes, including the computational output of Computing Miners, the performance of models from Model Miners, the data quality from Data Contributors, and the accuracy of inference service results. Validators must stake a certain amount of QTS tokens as a security deposit. Malicious validation behavior results in deposit slashing and revocation of validator status. Their rewards are tied to the fairness and accuracy of their assessments.

13th 3.2.5 Subnet Creators Subnet Creators can initiate vertical AI scenario subnets, defining task rules and incentive mechanisms within their subnet to attract participants from relevant domains. Creators earn a share of the transaction fees generated within their subnet and are responsible for ensuring its compliance and service quality. 3.2.6 Stakers QTS token holders can stake their tokens to support high-quality Validators and share in their reward distribution. Stakers can freely choose validator nodes, earning proportional rewards based on their staked amount. Staked tokens can be redeemed, subject to a lock-up period. 3.3 AI + Web3 Synergistic Use Cases 3.3.1 Decentralized Large Language Model (LLM) Training Platform This platform integrates distributed computing power from the Computing Power Subnet with privacy-preserving data from the Data Subnet, offering small and medium-sized developers a low-cost, high-efficiency service for training large models. Developers can access computational resources on-demand and utilize multi-party data via federated learning. Completed models can be verified as NFTs and listed for monetization within the Model Subnet.

14th 3.3.2 Privacy-Preserving Multimodal Data Collaboration Network This network establishes dedicated data collaboration channels for data-sensitive industries such as media, healthcare, and finance. Enterprises can jointly train multimodal data models without exposing core data, breaking down data silos and enhancing model generalization while ensuring data privacy and regulatory compliance. 3.3.3 Distributed AI Inference Service Marketplace This marketplace provides various AI applications with low-cost, highly reliable inference services. Application service providers, without needing to build their own computing clusters, can directly invoke the optimal combination of computing power and models from the Inference Service Subnet. This lowers the technical and cost barriers to application deployment, while on-chain verification of results guarantees the trustworthiness of inferences. 3.3.4 AI Model NFTization and Value Circulation Platform This platform enables Model Miners to mint their proprietary models or model optimization results/outcomes as NFTs for trading, leasing, or licensing within the ecosystem. Model NFTs not only serve as proof of ownership but can also be linked to revenue-sharing from model invocations, facilitating the long-term value circulation of AI model assets and incentivizing model innovation.

15th 4.1 Overall Architecture Design Quantum Surge employs a four-layer architecture designed to balance high performance, security, and scalability, meeting the complex demands of AI resource collaboration. 4.1.1 Consensus Layer Quantum Surge utilizes a hybrid consensus mechanism combining an enhanced Proof-of-Work (PoW) with AI performance verification. On one hand, PoW ensures verifiable resource contribution from nodes. On the other hand, an AI performance quantification module evaluates core metrics such as model inference accuracy, response speed, and robustness, enabling precise assessment of node service quality. This mechanism preserves the ecosystem's decentralization while filtering for high-quality AI service nodes, thereby enhancing overall network efficacy.

16th 4.1.2 Data Layer This layer comprises a distributed ledger and privacy-preserving computation protocols. The distributed ledger records all node contribution data, transaction information, and verification certificates, ensuring data immutability and traceability. Privacy-preserving computation protocols, based on federated learning and homomorphic encryption, establish secure data collaboration channels. These ensure raw data is never exposed during use while recording data contribution on-chain, providing the basis for value distribution. 4.1.3 Smart Contract Layer This layer deploys a series of smart contracts specifically designed for AI tasks, including Task Distribution Contracts, Result Verification Contracts, Reward Distribution Contracts, and Asset Verification Contracts. These smart contracts automate the matching and distribution of AI tasks, verify task completion quality, execute QTS token profit-sharing according to predefined rules, and facilitate on-chain verification of AI assets. The entire process requires no manual intervention, ensuring transparency and efficiency. 4.1.4 Application Layer This layer provides standardized API interfaces and an SDK toolkit, supporting developers in quickly integrating with the platform and building vertical AI subnets. It also includes a visual management dashboard, allowing Subnet Creators to manage nodes and adjust incentive rules easily. Furthermore, it offers ecosystem participants one-stop services for asset inquiry, task monitoring, and 收益 (reward) settlement. 4.2 Key Technical Solutions 4.2.1 Cross-Subnet Collaboration Protocol To enable resource scheduling and value transfer between different AI task subnets, the project has developed a proprietary Cross-Subnet Collaboration Protocol. Based on task requirements, this protocol can automatically invoke resources from multiple subnets. For example, a large model training task can simultaneously utilize computing power from the Computing Power Subnet, privacy-preserving data from the Data Subnet, and base models from the

17th Model Subnet. Upon task completion, the system automatically distributes rewards to participating nodes across subnets according to predefined ratios, achieving efficient multi-subnet collaboration. 4.2.2 AI Performance Verification Algorithm Quantum Surge has designed a model performance quantification evaluation system based on on-chain test datasets. The platform constructs standardized test datasets covering multiple modalities like text, image, and audio. The service effectiveness of Model Miners must be validated against these datasets. Validators compare model outputs with standard results, quantifying performance using metrics like accuracy, recall, and F1-score. This score is combined with metrics like response speed and stability to form a comprehensive rating, which serves as the core basis for reward distribution. 4.3 Dedicated Wallet & Toolchain 4.3.1 Dedicated Wallet The Quantum Surge Wallet is the core entry point to the ecosystem, emphasizing both security and usability. Its key functions include: ✓ Token Management: Supports the storage, transfer, and query of QTS tokens and major cryptocurrencies. It employs a Hierarchical Deterministic (HD) wallet structure combined with multi-signature and biometric technologies

18th to ensure asset security. ✓ AI Resource Trading: Enables users to initiate transactions for computing power leasing, model invocation, and data purchases directly within the wallet. Settlements are automated via smart contracts, eliminating the need for third-party intermediaries. ✓ Node Staking: Allows users to stake QTS tokens with high-quality validator nodes, view staking rewards in real-time, and conveniently execute staking and redemption operations. ✓ Cross-Subnet Identity Authentication: Incorporates a Decentralized Identity (DID) system. Users can access various subnets with a single wallet identity login, eliminating the need for repetitive registrations and ensuring identity uniqueness and security. 4.3.2 Developer Toolchain To lower the barrier to entry for developers, the project provides a comprehensive developer toolchain, including: ✓ Subnet Creation Tool: A visual platform for building subnets, allowing developers without extensive blockchain expertise to quickly create vertical AI scenario subnets and customize task rules and incentive mechanisms. ✓ AI Task Deployment SDK: A development toolkit supporting multiple languages like Python and Java. It provides standardized interfaces for computing power invocation, model deployment, and data integration, helping developers rapidly integrate AI tasks into the ecosystem. ✓ Performance Verification Plugin: An integrated tool for quantifying AI performance. Developers can pre-validate model performance locally, enhancing their model's competitiveness within the ecosystem.

19th 5.1 Decentralized Autonomous Organization (DAO) Architecture Quantum Surge adopts a DAO governance model to achieve democratic and transparent ecosystem management. The governance entities consist of three groups: Token Holders, Subnet Creators, and Core Validator Nodes, who jointly participate in decision-making and management. 5.1.1 Token Holders Token Holders are the core governance entities All users holding QTS tokens can participate in governance. Their voting weight is positively correlated with both the amount of tokens held and the duration they are staked, ensuring governance decisions align with the interests of the majority of participants.

20th 5.1.2 Subnet Creators As the core governors of vertical scenarios, Subnet Creators can propose specialized initiatives for rule adjustments within their subnets. They also participate in major ecosystem-wide decisions, ensuring the needs of their vertical scenarios are adequately represented.。 5.1.3 Core Validator Nodes Core Validator Nodes are comprised of validators who demonstrate high service quality and significant contributions within the ecosystem. They are responsible for conducting technical reviews on the feasibility of proposals, providing professional guidance to the community for voting, and overseeing the implementation process of approved proposals to ensure the effective execution of governance decisions. 5.2 Voting and Proposal Mechanism 5.2.1 Proposal Submission Ecosystem participants may submit proposals based on developmental needs, covering areas such as technical upgrades, rule adjustments, use of the ecosystem fund, and creation of new subnets. Proposals must include detailed descriptions, feasibility analysis, expected benefits, and other relevant content. Additionally, proposal submitters are required to stake a specified amount of QTS tokens as a proposal deposit. If a proposal fails to pass review, the deposit will not be refunded, serving as a deterrent against malicious proposals. 5.2.2 Proposal Review A review committee, consisting of Core Validator Nodes, will assess the technical feasibility, compliance, and ecological value of submitted proposals within seven business days. Proposals that pass the review will proceed to the community voting stage, while rejected proposals will receive detailed

21st feedback for revision. 5.2.3 Community Voting Approved proposals will be published on the DAO platform and opened for voting by community members. The voting period lasts for 14 business days. Voting weight is linked to the amount of QTS tokens staked by participants. To prevent large token holders from monopolizing voting power, a maximum voting weight per account is implemented. A proposal is considered passed if it receives more than 50% of the valid votes and the number of participating voters reaches at least 20% of the ecosystem's total user base. 5.2.4 Execution and Transparency Approved proposals will be implemented by the ecosystem execution team within the stipulated timeframe. The entire execution process will be transparently recorded on-chain. Upon completion, an execution report must be submitted for community oversight. If the implementation fails to achieve the expected outcomes, the community may initiate a review proposal to reassess the execution strategy.

22nd 6.1 Token Issuance Mechanism Project Name: Quantum Surge Token Symbol: QTS Token Standard: BEP-20 Total Supply:1.5 billion QTS. The supply is fixed with no inflationary mechanism, ensuring long-term value stability for the token. Token Issuance Price: $3.42 Token Allocation: Ecosystem Incentives (45%): 675 million QTS allocated to reward ecosystem participants such as computing miners, model miners, data contributors, and validators. Released linearly over 10 years to ensure sustainable long-term incentives. Development Team (10%): 150 million QTS allocated to the team. Tokens are locked for 24 months, followed by linear vesting over 36 months to align the team's long-term commitment to project development and operations. Foundation Reserve (20%): 300 million QTS reserved for core technology R&D, ecosystem partnerships, and contingency measures. Managed by the Foundation, with quarterly audit reports published to ensure transparent use of funds. Strategic Investment (15%): 225 million QTS allocated for private sales to compliant venture capital firms and strategic partners. Tokens are locked for 12 months, then released linearly over 24 months, providing capital for the

23rd project's initial development phase. Ecosystem Promotion (10%): 150 million QTS allocated for global marketing, developer grants, ecosystem application competitions, etc., to accelerate ecosystem growth and user acquisition. QTS is the sole utility token of Quantum Surge, enabling decentralized governance and community-driven development. Users can acquire QTS through three primary methods: participating in early product testing and community contributions to receive airdrop rewards, earning mining rewards by transacting on the Quantum Surge platform, and participating in single-token staking. 6.2 Token Utility Subnet Governance Voting: Token holders can participate in ecosystem DAO governance, voting on proposals related to subnet parameter adjustments, technical upgrades, and fund utilization. Voting weight is positively correlated with the amount of tokens held and the duration they are staked. Resource Transaction Payment: QTS is the primary medium of exchange for transactions within the ecosystem, including computing power leasing, model invocation, and data purchases. Users may receive discounts on transaction fees when paying with QTS.

24th Node Staking: Nodes, such as computing miners, model miners, and validators, are required to stake a certain amount of QTS tokens as collateral for service quality and compliant operation. Staking also enhances a node's priority in task allocation. Ecosystem Incentives: The ecosystem distributes additional QTS rewards to high-performing nodes, incentivizing active participation and fostering the healthy development of the ecosystem.

25th Dylan | CEO Former Managing Director of Enactus Capital and Partner at Zonff Capital, Dylan has focused extensively on research and investment within the internet finance sector, successfully investing in numerous high-quality projects across segments like installment payments, cash loans, and auto loans. An early member of the Bitcoin Talk community and the Xdag Chinese community, and a partner at AlphaCoinFund, he advocates for "code as law, privacy as freedom, and computation as rights." Dylan has made early-stage investments in over a dozen blockchain projects including Rsk, Celer, Box, and Dcc. He has also made strategic investments in mining farms, mining pools, wallets, exchanges, and media entities. At Quantum Surge, he oversees finance, fundraising, and external partnerships. Joson Yeo CTO/ Head of Operations Joson possesses extensive technical and project management expertise, with research interests encompassing cryptography, algorithm theory, and the principles, design, application, and implementation of blockchain technology. With years of C/C++ development experience, he excels in technical research, problem-solving, and innovation. Originally a systems architect, he has designed and implemented large-scale, concurrent, layered server architectures. Currently serving as the Chief Operating Officer and Technology Director at Quantum Surge, he leads the research and development of cross-chain infrastructure technology.

26th Miguel Benedict CTO Holding a Ph.D. in Computer Science and a Ph.D. in Finance from Stanford University, Miguel is an expert in fintech core technology. In blockchain, he has contributed to achieving a world record for settlement times (15 seconds), surpassing the conventional international standard of 2 minutes. After years at Google in Silicon Valley (UK), he transitioned into the financial investment sector, playing a crucial role in the nascent and expansive blockchain investment market. He currently serves as the Chief Technology Officer. Backson CMO With a Master's degree in Computer Science, Backson entered the cryptocurrency space in 2012. He is a seasoned figure within the U.S. blockchain community, specializing in technical analysis of cryptocurrencies and is a major holder of various digital assets. At Quantum Surge, he is responsible for global business development partnerships and managing listings on global cryptocurrency exchanges.

27th Chapter 8 Project Development Roadmap The core objective is to complete the research and development of the core technology architecture. 2026 Q1 Foundation Establishment  Platform official launch.  Establish foundational compliance framework and initial ecosystem structure.  Partner with 2-3 third-party compliance institutions and regulatory technology (RegTech) firms to build an AI resource compliance review system and risk interception module, ensuring compliant ecosystem operations.  Complete pilot deployments of the Computing Power Subnet and Model Subnet, onboard 100 core computing nodes and 50 high-quality model nodes, and initiate small-scale AI task collaboration testing. 2026 Q2 – Technology Integration  Mainnet public chain officially launches. QTS token distribution commences in compliance with regulations. The DAO governance platform goes live simultaneously, initiating community governance.  Official release of the Quantum Surge wallet, featuring enhanced privacy computing and on-chain asset verification capabilities. Support for multiple languages and regional compliance adaptations. User base surpasses 50,000.  Host a global AI developer competition with a dedicated "Compliant Innovation Track," focusing on supporting high-quality projects with minimal compliance risks. Attract over 500 development teams to join the ecosystem.

28th 9.1 Compliance Statement 1.Service Scope: Quantum Surge solely provides underlying technological infrastructure for AI resource collaboration. It does not directly participate in AI content creation or application, nor does it involve the processing of any illegal information. The project steadfastly adheres to the legal and regulatory frameworks of all global jurisdictions and resolutely opposes all forms of non-compliant AI applications. 2.Ecosystem Participant Obligations: All developers and nodes joining the ecosystem must strictly comply with the laws and regulations of their own jurisdiction and the jurisdictions they serve. They must complete necessary qualification verifications, sign a "Compliance Operation Pledge," and are strictly prohibited from developing or providing illegal AI services involving pornography, gambling, fraud, or other illicit activities. Participants must accept compliance audits and community oversight within the ecosystem. 3.Compliance Risk Management Framework: The project implements a comprehensive, full-process compliance and risk control system covering "pre-screening, real-time monitoring, and post-event accountability." This includes compliance reviews for all incoming nodes and applications prior to entry; real-time monitoring of on-chain transactions and AI service content during operation using technical tools to intercept violations; and punitive measures for violators post-event, including security deposit forfeiture, node disqualification, and inclusion in industry blacklists. Severe cases will be referred to judicial authorities. 4.Proactive Safeguards and Cooperation: The project utilizes technical

29th measures such as IP geolocation restrictions, Know-Your-Customer (KYC) verification, and content moderation to prevent unauthorized access from prohibited regions and protect the rights of vulnerable groups, including minors. Quantum Surge proactively cooperates with global regulatory agencies in law enforcement actions, voluntarily providing on-chain data query services and evidence preservation to assist in combating AI-related criminal activities. 9.2 Risk Disclosures Participation in the Quantum Surge ecosystem and use of its related services entail the following risks. Participants should fully understand these risks and exercise caution in their decision-making: Technical Risk: Despite implementing multiple layers of security protocols, blockchain technology remains vulnerable to risks such as hacker attacks, algorithmic vulnerabilities, and node failures. AI models may also suffer from performance fluctuations and data biases. These factors could lead to anomalies in on-chain data, loss of user assets, or failure to meet service quality standards. Market Risk:The market price of the QTS token is influenced by numerous factors including industry policies, market supply and demand, competitive landscape, and macroeconomic conditions, and may experience significant volatility, posing a risk of asset depreciation. Furthermore, the rapid pace of technological iteration in the AI industry presents the risk of diminished market competitiveness if the project's technological roadmap fails to keep pace with industry developments. Regulatory Risk: Legal definitions and regulatory policies concerning blockchain technology, cryptocurrencies, and the AI industry vary significantly across global jurisdictions and are subject to change. Such policy adjustments could adversely affect the ecosystem's operational model, token circulation, and scope of AI services. Participants bear the responsibility for understanding and managing compliance risks arising from regulatory changes. Ecosystem Risk: Ecosystem development may face challenges such as insufficient developer participation, slow user growth, or suboptimal cross-subnet collaboration efficiency. Failure to establish a virtuous cycle could impact the project's long-term viability. Additionally, malicious competition within the industry and negative public sentiment could adversely affect the ecosystem's brand reputation and operations.

30th Compliance-Related Risk: Although the project has established a robust compliance framework, there remains a risk that individual nodes or developers may circumvent rules to engage in illegal or non-compliant activities. Such actions could result in user asset loss, account suspension, or other consequences. Participants must enhance their ability to identify risks and refrain from participating in any non-compliant ecosystem activities. This whitepaper does not constitute investment advice, a financing solicitation, or a participation offer.Participants should engage in ecosystem activities and related endeavors rationally and under the premise of fully understanding the associated risks and complying with all applicable laws and regulations in their jurisdiction. All risks and responsibilities are borne by the participants themselves. Any observed violations can be reported through official ecosystem channels to jointly uphold the compliant and healthy development of the ecosystem.

31st Amidst the era of deep integration between AI and Web3 technologies, Quantum Surge presents a novel solution to address the resource barriers and imbalanced value distribution inherent in the traditional AI industry. This is achieved through its innovative layered subnet architecture, AI-powered consensus incentive mechanism, and solutions for privacy-preserving computation and on-chain asset verification. The Quantum Surge project not only builds an open, collaborative infrastructure for global AI developers but also reshapes the value distribution system of the AI industry, empowering every ecosystem participant to create and share value through collaboration. Moving forward, Quantum Surge will continue to deepen its technological research and development, refine its ecosystem strategy, and strengthen its compliance governance. We will work hand-in-hand with global developers, computing power providers, data contributors, and application service providers to collectively construct a decentralized, inclusive, and sustainable AI value network. We firmly believe that through technological innovation and collaborative ecosystem-building, Quantum Surge will emerge as core infrastructure for the decentralized transformation of the AI industry. It will drive AI technology to better serve socio-economic development, inaugurating a new chapter in the co-creation of AI value.