Author: Zhang Feng

Currently, artificial intelligence is deeply integrated into social production and life in unprecedented ways, and its security and governance systems form the cornerstone of the digital age. However, a revolution in computing power based on physical principles—quantum computing—is quietly approaching, and its potential disruptive power poses a serious challenge to existing security defenses and governance frameworks. Will quantum computing overturn the current AI security and governance systems? This is not only a technical question but also a global challenge concerning the order of future digital society. When a leap in computing power meets lagging rules, how do we prepare for "Q-Day" in advance?

1. How does quantum computing threaten the widely used asymmetric encryption algorithms?

The security of current AI systems relies heavily on asymmetric encryption algorithms represented by RSA and ECC (Elliptic Curve Cryptography) for model transmission, data storage, and identity authentication. The security of these algorithms is based on the "computational complexity" of mathematical problems like "integer factorization" or "discrete logarithm," which classical computers cannot solve within a reasonable timeframe.

However, quantum computing brings a fundamental paradigm shift. Quantum algorithms represented by Shor's algorithm can theoretically reduce the solution time of these problems from exponential to polynomial scale. A research paper points out that the latest quantum algorithms, including those based on Regev's algorithm and its extensions, are continuously optimizing the efficiency of cracking asymmetric encryption. This means that once sufficiently scaled (typically referring to a universal quantum computer with millions of stable qubits), the "locks" currently protecting internet communication, digital signatures, and encrypted data may be opened instantly.

This threat is not far-fetched. Research from the Zhiyuan community warns that this is a "present tense" threat: attackers can start intercepting and storing encrypted communication data (including AI training data, model parameters, etc.) now and wait until future quantum computers mature to decrypt them. This strategy of "intercepting first, decrypting later" exposes all high-value information that needs long-term confidentiality, including national secrets, commercial patents, and personal privacy data, to future risks. Therefore, the threat posed by quantum computing to asymmetric encryption is foundational and systemic, directly undermining the security system of current AI and the entire digital world.

2. What new challenges do AI model training and data privacy protection face in the face of quantum computing?

The development of AI relies on massive data feeding and complex model training, which is itself filled with privacy and security challenges. The intervention of quantum computing sharpens and complicates these challenges.

First, the long-term confidentiality of the data lifecycle becomes invalid. As mentioned, current AI training datasets encrypted and stored in the cloud or during transmission may be completely exposed due to future quantum decryption. The global quantum migration strategy white paper from Xi'an Jiaotong-Liverpool University explicitly points out that adversaries worldwide are organizing to implement this "data harvesting" strategy, patiently awaiting the arrival of "Q-Day" (the day quantum computers become practical). This poses a foundational threat to AI models that rely on sensitive data such as medical records, financial information, and biometric features for training.

Second, privacy-preserving computing technologies like federated learning face new tests. Federated learning protects original data by training models locally and only exchanging model parameter updates. However, these exchanged gradients or parameter updates are also transmitted securely. If the underlying encryption is breached by quantum computing, attackers can reverse-engineer the original data features of the participating parties, rendering the privacy protection mechanism ineffective.

Lastly, the difficulty of model theft and intellectual property protection surges. Mature AI models are core assets for enterprises. Currently, model weights and architectures are usually distributed and deployed through encryption. Quantum computing may render these protective measures ineffective, leading to easy copying, reverse engineering, or tampering of models, resulting in serious intellectual property infringement and security vulnerabilities. The China Academy of Information and Communications Technology emphasizes in its "Blue Book on AI Governance" that AI governance must address risks like technological abuse and data security, and quantum computing undoubtedly amplifies the destructive power of these risks.

3. How will the development of quantum machine learning impact the AI security and ethics review framework?

The combination of quantum computing and AI—quantum machine learning (QML)—signals a new round of performance breakthroughs. At the same time, it brings unprecedented new security and ethical issues, impacting existing review frameworks.

In terms of security, QML may give rise to more powerful attack tools. For instance, quantum algorithms could significantly speed up the generation of adversarial samples, crafting more hidden and destructive attacks, rendering the current AI security defense systems based on classical computing (such as adversarial training and anomaly detection) quickly obsolete. Some analyses refer to "quantum + AI" as the next battleground for cybersecurity, indicating the need to proactively improve related regulatory frameworks.

From an ethical perspective, the "black box" nature of QML may be even more obscure than classical AI. Its decision-making process is based on quantum superposition and entanglement states, making it potentially harder to explain, audit, and hold accountable. Ethical debates and risks related to algorithmic fairness, accountability, and technological controllability brought by QML have already been discussed extensively. How can existing AI ethical principles (such as transparency, fairness, and accountability) be applied on a quantum scale? How can regulatory bodies audit a decision model based on quantum circuits that may exist in multiple superimposed states? These are challenges that the current ethical review framework is not yet prepared for. The governance model needs to shift from pure technical compliance to a deeper understanding of the essence of quantum characteristics and their social impacts.

4. Can existing AI governance regulations (such as GDPR) cope with the security changes brought by quantum computing?

Current AI and data governance regulations, represented by the EU General Data Protection Regulation (GDPR), have core principles such as "privacy by design and by default," "data minimization," "storage limitation," and "integrity and confidentiality," which still hold guiding value on the conceptual level. However, they are facing a "compliance gap" due to the technical implementations and compliance requirements inspired by quantum computing.

GDPR requires data controllers to take appropriate technical and organizational measures to ensure data security. But in the context of quantum threats, what constitutes "appropriate" encryption measures? Continuing to use algorithms that have proven to be quantum insecure may likely be deemed as failing to fulfill security obligations in the future. How can the time constraints for data breach notifications in the regulations be effectively enforced when faced with high-level attacks initiated by quantum computing that may be completed instantly and leave no traces?

Legislators worldwide have recognized the necessity for reform. The "2025 Global Artificial Intelligence Governance Report" shows that countries are accelerating the development of specialized AI governance laws and establishing high-level coordinating bodies. China emphasizes the need to "accelerate the improvement of the data foundational system" and continues to advance the "AI+" initiative in the "Digital China Development Report (2024)." These developments suggest that governance systems are actively adjusting. However, regulations specifically targeting the intersection of "quantum computing + AI" are nearly still blank. Existing regulations lack provisions for post-quantum cryptography migration timelines, QML model auditing standards, and quantum-era data security classification, making it difficult to effectively address the impending security changes.

5. What are the application prospects and implementation challenges of post-quantum cryptography in AI systems?

The most direct technical solution to address quantum threats is post-quantum cryptography (PQC). PQC refers to cryptographic algorithms that can withstand attacks from quantum computers. It is not based on quantum principles but rather on new mathematical problems believed to be difficult for quantum computers to solve quickly (such as lattices, coding, multivariate, etc.).

The application prospects of PQC in AI systems are broad and urgent. PQC can be used to protect every aspect of the AI workflow: encrypting training data and model files with PQC algorithms; using PQC digital signatures to verify the integrity and authenticity of model sources; establishing PQC secure communication channels between distributed AI computing nodes. Fortinet points out that PQC is not a distant concept but an urgent practical solution needed to protect digital systems from potential quantum threats.

However, the comprehensive implementation of PQC faces significant challenges:

Performance and compatibility challenges: Many PQC algorithms have key sizes, signature lengths, or computational overheads far greater than existing algorithms, which may create performance bottlenecks when integrated into AI training and inference processes sensitive to computational efficiency and latency. Additionally, all related hardware, software, and protocol stacks need to be upgraded to ensure compatibility.

Complexity in standards and migration: Although organizations like the US NIST are advancing PQC standardization processes, determining final standards and achieving global uniformity still need time. The commercial encryption forefront dynamics released by the Beijing Municipal Administration of Communications highlight that the industry is actively implementing open-source NIST candidate algorithms to help various industries cope with threats. The entire migration process is a vast and complex system engineering project involving risk assessments, algorithm selections, mixed deployments, testing, and complete replacements, particularly challenging for complex AI ecosystems.

New security risks: PQC algorithms are relatively new research fields, and their long-term security has not yet undergone the decades-long practical cryptanalysis testing that RSA experienced. Rushing to deploy potentially vulnerable PQC in AI systems also poses a risk.

6. It is dangerous to passively wait for "Q-Day" amid this transformation

The disruptive impact of quantum computing on the current AI security and governance systems is real and imminent. It does not completely overturn the existing framework but rather undermines its cryptographic foundations, amplifies data risks, complicates ethical issues, and highlights regulatory obsolescence, forcing the entire system to undergo profound and forward-looking upgrades.

In light of this transformation, passively waiting for "Q-Day" is dangerous. We recommend taking the following actionable paths:

Initiate quantum security risk assessments and inventory compilation: Immediately conduct quantum threat assessments on core AI assets (especially those involving long-term sensitive data such as models and datasets) to identify the most vulnerable areas and establish a priority list for migration.

Develop and implement a PQC migration roadmap: Pay attention to the progress of standard organizations like NIST and start planning for PQC integration in the development and operation of AI systems. Prioritize the adoption of "cryptographic agility" designs in new and critical systems to facilitate seamless algorithm replacements in the future. A hybrid encryption mode currently using "classical + PQC" can be considered as a transition.

Promote adaptive updates to governance frameworks: Industry organizations, standard bodies, and regulators should collaborate to study and integrate quantum-resistant requirements into AI security standards, data protection regulations, and product certification systems. Establish research frameworks and guidelines for the ethical review of QML in advance.

Strengthen interdisciplinary talent cultivation and research: Cultivate interdisciplinary talents who understand both AI and quantum computing and cryptography, encourage the inclusion of quantum threat models in AI security research, and fund the development of anti-quantum AI security technologies.

The challenges brought by quantum computing are immense, but they also provide us with an opportunity to reassess and fortify the foundations of the digital world. Through proactive planning, collaborative innovation, and agile governance, we have the potential to build a more resilient AI future that can embrace the benefits of quantum computing while withstanding its security risks.

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