According to William Fry’s Barry Scannell, quantum computing could enhance AI dramatically, but it’s not without its own security challenges.
Microsoft recently announced a major breakthrough in quantum computing with its Majorana 1 qubit-based chip, significantly advancing the reliability and scalability of quantum processors.
This development marks a crucial step toward fault-tolerant quantum computing, overcoming one of the most significant barriers to practical applications. By demonstrating a more stable quantum system with reduced error rates, Microsoft’s research paves the way for quantum computing to move beyond experimental stages and into real-world implementation.
This breakthrough has profound implications for artificial intelligence (AI), as quantum computing is expected to dramatically enhance AI’s capabilities. It could make AI systems more powerful, efficient and capable of solving problems that classical computers cannot handle.
However, while quantum computing holds enormous potential, it also introduces fundamental challenges, particularly in data security and encryption.
Existing cryptographic methods, such as Rivest Shamir Adleman (RSA) and Elliptic-curve cryptography (ECC), rely on the difficulty of factoring large prime numbers, a task that quantum algorithms could break with ease. This means that sensitive data, financial transactions and AI-driven decision-making systems could become vulnerable to quantum-enabled cyber threats.
At the same time, quantum AI could provide solutions to these risks by advancing quantum-safe encryption techniques and improving cybersecurity. As this technology develops, regulatory bodies and industry leaders must work together to ensure that quantum-powered AI remains secure, ethical and aligned with global data protection laws.
Quantum computing’s impact on AI
Quantum computing is poised to redefine AI, offering computational power that surpasses even the most advanced classical supercomputers. While quantum computing has demonstrated theoretical advantages, practical applications in AI are still in the early stages of research and development.
Companies such as IBM, Google and Microsoft are actively exploring ways to integrate quantum capabilities into AI workflows, but large-scale adoption remains years away.
If scalability and error correction challenges are overcome, quantum-enhanced AI could drive breakthroughs in fields such as drug discovery, financial modelling, autonomous decision-making and cybersecurity.
As these technologies converge, they introduce significant regulatory and legal challenges, particularly in the context of the EU AI Act, which will play a key role in governing AI deployment across Europe.
Accelerated machine learning and model training
One of the most immediate benefits of quantum computing for AI lies in machine learning and model training. AI development today is constrained by the sheer computational power required to train deep learning models.
Quantum algorithms have the potential to significantly reduce training times by handling multiple computations simultaneously. This could lead to more efficient pattern recognition and predictive analytics.
However, the practical implementation of quantum computing in AI remains an open challenge. Current quantum processors face high error rates and relatively low qubit counts, meaning that while quantum AI is a promising area of research, it has yet to demonstrate real-world superiority over classical systems.
Expanding AI’s problem-solving capabilities
Beyond speed and efficiency, quantum computing will expand AI’s ability to tackle highly complex problems.
In pharmaceutical research, for example, IBM Quantum and major pharmaceutical firms have successfully applied quantum-enhanced AI to simulate molecular interactions and protein folding. This has significantly accelerated drug discovery and could revolutionise the healthcare industry.
In finance, institutions such as Goldman Sachs and JPMorgan are researching quantum algorithms for risk modelling and portfolio optimisation.
Although full-scale quantum-driven financial modelling remains experimental, quantum AI has the potential to process financial data at speeds unimaginable with classical computing. This could fundamentally reshape investment strategies and financial risk assessment.
Post-quantum encryption and the race to secure data
One of the most pressing concerns in the quantum AI era is the vulnerability of existing encryption methods. The cryptographic infrastructure that secures global financial systems, medical records and government communications is based on algorithms such as RSA, ECC and Diffie-Hellman key exchange, all of which could be broken by a sufficiently advanced quantum computer.
To address this threat, researchers are developing post-quantum cryptographic (PQC) algorithms, which are designed to resist quantum attacks.
In July 2022, NIST announced the first four quantum-resistant encryption algorithms, including CRYSTALS-Kyber and CRYSTALS-Dilithium, which are expected to replace RSA and ECC as the standard for secure encryption.
In case it wasn’t abundantly clear how nerdy this area is, and in case one wonders where these names came from: Kyber crystals are what give light sabres their colour light in the Stars Wars franchise and dilithium crystals are used in the warp drives of starships in the Star Trek series.
The US is taking an aggressive stance on quantum computing and AI, viewing it as both an economic opportunity and a national security priority. The National Quantum Initiative Act, signed into law in 2018 and expanded under subsequent administrations, has positioned the US as a leader in quantum research and development.
The US has already mandated that all federal agencies begin transitioning to PQC algorithms, ensuring that critical government data remains secure.
The transition to post-quantum encryption is expected to take years, as governments and private-sector organisations migrate their existing security infrastructure to quantum-resistant algorithms.
Businesses handling sensitive customer data, especially in sectors such as finance, healthcare and cloud computing, are being urged to begin preparing now by implementing hybrid cryptographic approaches that combine classical and post-quantum encryption.
Regulatory oversight under the AI Act and GDPR
The EU AI Act is one of the first legislative frameworks governing AI, setting stringent requirements for high-risk AI systems, transparency obligations and safeguards against AI-related harm.
While the act does not yet contain explicit provisions for quantum-enhanced AI, its broad definitions mean that any AI system employing quantum computing could fall under its scope, particularly in critical applications such as finance, healthcare and national security.
Quantum advantage in AI training could accelerate model capabilities beyond classical limitations, potentially rendering the threshold obsolete or requiring an adjusted regulatory approach.
If scalable, fault-tolerant quantum systems emerge, they could enable exponential increases in AI processing power, making systemic risk assessments far more complex and necessitating new governance mechanisms to address unpredictable advancements in AI capability.
Beyond the AI Act, the General Data Protection Regulation (GDPR) presents additional challenges for quantum AI, particularly concerning anonymisation and data security.
The European Data Protection Board (EDPB) has issued guidelines on anonymisation, stating that for data to be considered truly anonymous under GDPR, it must be processed in such a way that re-identification is impossible. However, quantum computing poses a major challenge to anonymisation techniques.
Shaping the future of AI with quantum computing
Quantum computing is no longer a distant theoretical concept but an emerging force that will redefine AI. While the integration of quantum computing into AI holds enormous potential, much of its impact remains in early research stages.
The EU AI Act represents one of the first attempts to provide a structured regulatory framework for AI, but it will need to evolve to fully address the implications of quantum-enhanced AI.
The conversation surrounding quantum AI is no longer hypothetical. Its development is happening now, and its implications will shape the future of AI governance worldwide.
Barry Scannell is a partner in William Fry’s Technology department specialising in artificial intelligence, copyright, IP and data protection.
Don’t miss out on the knowledge you need to succeed. Sign up for the Daily Brief, Silicon Republic’s digest of need-to-know sci-tech news.
