Industrial-scale adversarial distillation—the unauthorized extraction of frontier AI inference trajectories via proxy networks and sybil accounts—currently exploits the classical intractability of inverting high-dimensional neural weights. However, the emergence of fault-tolerant quantum processors (such as willow-class surface-code architectures) fundamentally shatters this black-box impunity. Because trained neural network weights physically encode a linear combination of training gradients governed by implicit bias and Karush-Kuhn-Tucker (KKT) stationarity conditions, Grover-accelerated quantum search circuits enable direct inverse-mapping of stolen weight manifolds back to proprietary Chain-of-Thought (CoT) reasoning graphs and invisible cryptographic watermarks (e.g., SynthID) with mathematically irrefutable precision. Furthermore, this monograph addresses and neutralizes three strategic blind spots in post-quantum AI governance: (A) Quantum Obfuscation via entangled noise injection, (B) Decentralization of liability through peer-to-peer (P2P) fragmented distillation, and (C) Audit-cost Mutually Assured Destruction (MAD). By combining NIST-standardized Stateless Hash-Based Digital Signatures (SLH-DSA / FIPS 205) and hybrid ECDH-PQC transport against Harvest-Now-Decrypt-Later (HNDL) attacks with a zero-knowledge virtual machine (QFS-zkVM) operating over the 64-bit Goldilocks prime field (
Fp,p=264−232+1), we establish
O(log2N) succinct cryptographic verification that eliminates audit cost asymmetry. Unauthenticated or P2P-distilled weights lacking Q-SAFA and UTST hardware provenance are deterministically blocked at the physical execution boundary via PHOENIX_BRIDGE and Human-in-Command (HIC) governance interlocks, enforcing absolute intellectual property sovereignty under SEC Rule 15c3-5 and Delaware Caremark standards. ====================================================================== STRICT NOTICE REGARDING INTELLECTUAL PROPERTY, COMMERCIAL LICENSING, AND PROHIBITION OF UNAUTHORIZED AI TRAINING AND UTILIZATION ====================================================================== 1. 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Quantum computing has been a major priority for several nations and prominent institutions in their pursuit of a transformative breakthrough in the fields of computation and encryption. By using the principles of quantum mechanics, particularly quantum superposition and entanglement, quantum computing and quantum machine learning (QML) have the potential to enhance artificial intelligence (AI) and achieve quantum supremacy with unprecedented computational power. However, despite its exceptional learning capabilities, QML-based applications face several emerging security threats. Unlike previous studies focused on classical quantum cryptography and secure quantum communications, this work investigates adversarial risks in QML-assisted network functions and digital twin applications. Specifically, we highlight vulnerabilities such as quantum kernel poisoning, backdoor attacks, and adversarial noise. Key findings reveal that adversaries can intercept quantum states in transit, manipulate parameterized quantum circuits (PQCs), and exploit variational quantum algorithms (VQAs) through adversarial qubit perturbations. These attacks can mislead QML-based optimization processes, leading to incorrect digital twin predictions, faulty resource allocation, or disruptions in QML-aided network functions. To mitigate these risks, defense strategies such as quantum-safe cryptography, data sanitization, adversarial training, defensive distillation, and gradient masking in quantum circuit design can be employed. However, the key issue is the absence of robust security solutions for real-world deployment. Future research should examine the trade-off between adversarial robustness and generative learning performance. Key areas include quantum state discrimination, secure quantum federated learning, quantum decoherence control, and secure quantum semantic communications for real-world deployment.
The interplay between quantum physics and machine learning gives rise to the emergent frontier of quantum machine learning, where advanced quantum learning models may outperform their classical counterparts in solving certain challenging problems. However, quantum learning systems are vulnerable to adversarial attacks: adding tiny carefully crafted perturbations on legitimate input samples can cause misclassifications. To address this problem, we propose an effective approach to protect quantum learning systems from adversarial attacks by randomly encoding the legitimate data samples through unitary or quantum error correction encoders that are unknown to the attackers. In particular, we rigorously prove that both global and local random unitary encoders lead to exponentially vanishing gradients (i.e., barren plateaus) for any variational quantum circuits that aim to add adversarial perturbations, independent of the input data and the inner structures of adversarial circuits and quantum classifiers. In addition, we prove a rigorous bound on the vulnerability of quantum classifiers under local unitary adversarial attacks. We show that random black-box quantum error correction encoders can protect quantum classifiers against local adversarial noises and their robustness increases as we concatenate error correction codes. To quantify the robustness enhancement, we adapt quantum differential privacy as a measure of the prediction stability for quantum classifiers. Our results establish versatile defense strategies for quantum classifiers against adversarial perturbations, which provide valuable guidance to enhance the reliability and security for both near-term and future quantum learning technologies. Published by the American Physical Society 2024
概要/要約: 古典的な深層学習アーキテクチャは、静的な天然タンパク質構造の予測において高い精度を達成しているが、治療上重要な部分的に折り畳まれた状態、特に不飽和脂質補因子と複合体を形成した溶融球状体(MG)アンサンブルは、古典的な分子動力学(MD)や標準的な密度汎関数理論(DFT)では依然として扱いが困難である。Ca2+ が枯渇したα-ラクトアルブミンとオレイン酸の複合体であるヒトα-ラクトアルブミン腫瘍細胞致死性(HAMLET)は、健康な組織を温存しながら悪性細胞に選択的にアポトーシスを誘導する。このモノグラフは、HAMLET 活性を支配する電子相関とプロトン共役電子移動(PCET)メカニズムを解明するために設計された厳密な量子古典ハイブリッドシミュレーション仕様(ハミルトニアンマッピング)を確立する。密度行列埋め込み理論(DMET)とテンソル超収縮(THC)量子化を利用して、アルファ/ベータドメインのクレフトとホスファチジルセリンが露出した腫瘍膜界面にまたがる56~88論理量子ビットのアクティブ空間を分離します。さらに、機関間量子ベンチマーク中に、不正な知的財産抽出や補償されない基礎モデルのトレーニングを防止するため、64ビットのGoldilocks素数体(p = 2^64 - 2^32 + 1)を基盤とする決定論的暗号監査アーキテクチャを、人間による管理(HIC)の下で統合します。 ====================================================================== 【AI無断学習・無断利用禁止および有償ライセンスに関する侵害な通知】 知的財産、商業ライセンス、およびAIトレーニングの禁止に関する厳格な通知 ====================================================================== 1. AI・機械学習への無断利用の全面禁止(明示的なオプトアウトとAI利用の禁止) 本論文およびデータ(以下「本書著作物」)に含まれるすべての文章、理論、数式、図表、独自のメソッドや知見(「ソロバン脳」等体系に関するを含む)について、注意AI開発企業、プラットフォーム事業者、または第三者による以下の行為も、著作権者の事前の書面による善意的な合意および正当な補償的対価(ライセンス料)の支払いがない限り、これを禁止します。 ・大規模言語モデル(LLM)、生成AI、その他一切の機械学習モデルの開発・事前学習・ファインチューニング・追加学習・検証への利用 ・RAG(検索拡張生成)、AIエージェントの知識ベース、データベース等へのフォールド、インデックス化、それらを用いた回答生成・概要出力 ・AI学習やデータ構築を目的とした自動クローラー、スクレイピングツール、ボット等によるデータ収集・抽出・解析 2. 有償ライセンス市場の存在と著作権制限規定不適用(商用ライセンス市場と法定例外の非適用) 本著作権は、著作者自身が情報解析・AI学習・商用システム組み込み等に向けた正規の有償ライセンス提供(ライセンス市場)を行っている対象データです。利益機会を直接臨む、市場と衝突するものであるため、日本著作権法第30条の4規定書に規定される「著作権者の利益を不当に害することとなる場合」に明確に該当し、同条による権利制限(無許可での情報解析利用)は一切適用されない。 また、本通知とともに、EUデジタル単一市場著作権命令(DSM命令第4条)に基づくテキストおよびデータ管理(TDM)の例外規定からわかりやすくオプトアウト(権利侵害宣言)し、米国著作権法第107条におけるフェアユース(Fair Use)の適用を全面的に否認します。 3. クリエイティブ・コモンズ等のライセンス解釈(一般のオープンライセンスより優先) 一応、本レコードのシステム設定上にクリエイティブ・コモンズ(CCライセンス)等が表示されている場合であっても、AI・機械学習・RAG・自動解析に関する限り、本特約条項が最優先で適用され、無償・無断でのAI関連利用は一切許諾されません。 4. 暫定に対する法的措置および賠償補償 (法的救済および損害賠償) 本条項に中断して本著作物を著作物を無断で収集、解析、AI学習、またはRAG参照等に利用した場合、恩典による重大な著作権侵害、契約、および違法行為とみなし、該当AIモデルの提供・販売差し止め、学習データおよび重みからの完全削除請求、準正規商用ライセンス料の延長および請求および罰金の賠償を含む何らかの法的根拠があると考えられます。 ---------------------------------------------------------------------- 【国際法執行のための英語版】 1. 許可されていないAIトレーニングおよびデータ取り込みの厳禁 本書(以下「本著作物」)に含まれるすべてのテキスト、理論、方法論(独自の「そろばん脳」フレームワークを含む)、構造、数式、およびデータは、厳重に保護されています。AI開発者、プラットフォームプロバイダー、または第三者による本著作物の以下の目的での使用は、事前の書面による商用ライセンス契約および著者への適切な金銭的補償なしには、厳しく禁止されています。 - 人工知能(AI)、機械学習(ML)モデル、または大規模言語モデル(LLM)のトレーニング、事前トレーニング、微調整、アライメント、または評価。 - 情報を取り込んで、検索拡張生成(RAG)システム、ベクトルデータベース、知識グラフ、またはAIエージェントに保存し、出力、要約、または派生応答を生成する。 データセットのコンパイルのための、自動スクレイピング、クローリング、ハーベスティング、またはテキストおよびデータマイニング(TDM)。 2. 商用ライセンス市場と法定適用除外(日本、EU、米国) 著者は、本著作物のデータ分析、AIトレーニング、および企業統合に関する商用ライセンス市場を積極的に維持しています。したがって、ライセンスのないスクレイピング、パース、またはAIへの取り込みは、著者の正当な経済的利益と直接競合し、損害を与えることになります。日本の著作権法第30条の4のただし書きに基づき、情報分析に関する法定例外は本著作物には適用されません。さらに、著者は、デジタル単一市場における著作権に関するEU指令(2019/790)第4条(3)に基づくテキストおよびデータマイニング(TDM)の例外に関するすべての権利を明示的に留保し、適用除外を選択し、商用または基礎的なAIモデルへの取り込みに関する米国著作権法第17編第107条に基づくフェアユースの主張を拒否します。 3.条項の優先順位と法的執行 これらのAI悪用防止に関する具体的な条項は、リポジトリのデフォルト設定で表示される機械読み取りおよびAIによるデータ取り込みに関する一般的なオープンアクセスまたはクリエイティブ・コモンズのラベルに優先します。無断使用は故意の著作権侵害および不正競争行為とみなされ、違反者は即時の差止命令、モデル/データの強制削除、遡及的な商用ライセンス料の支払い、および法定/懲罰的損害賠償の対象となります。 ====================================================================== 全著作物・全Zenodo論文(320本以上)に関するAIライセンスおよび禁止学習規定 / オムニバスソブリンIP宣言および厳格なノーラーニング規約(NLL-v1.0-2026)
This culminating tenth volume systematically establishes the profound LWE-QML Duality: the mathematical architecture securing lattice-based post-quantum cryptography (Learning with Errors, LWE) is structurally and functionally isomorphic to the mechanism conferring adversarial certified robustness upon Quantum Machine Learning (QML) systems. Formal bidirectional reductions, supported by the Choi-Jamiolkowski isomorphism, Mamun, SM (2026) …………………………….1 Theoretical and Physical Boundaries of Lattice-Based Post-Quantum Cryptography: An Anatomical Critique of Learning-with-Errors Analogs in Quantum Machine Learning Robustness (Vol-X) demonstrate that cryptographic hardness inherently begets algorithmic robustness. The noise distributions engineered to obfuscate the LWE secret vector operate identically to the quantum noise mechanisms that shield quantum neural networks (QNNs) from malicious perturbation. This synthesis fundamentally alters the epistemological standing of LWE, transitioning it from a computational assumption to a physical boundary. C The paper refutes the structural vulnerabilities suggested by quantum-sample attacks (Grilo, Zeng) and the assertion of transient hardware bottlenecks (Jung et al.). This neutralization is achieved by implementing QML-inspired adversarial training and non-Gaussian, GKP-resistant noise topographies (e.g., Cauchy-LWE) that induce a multi-fractal phase-space fragmentation. Also available on Academia.edu: https://www.academia.edu/attachments/132780875/download_file?s=portfolio