Quantum: The Regulatory Frontier That Will Catch Us Off Guard

Opinion
Written By Giulia Paggiola

Yesterday, I met Sabrina Maniscalco, CEO of Algorithmiq, at the Italian Tech Forum in Zurich. They develop quantum algorithms for life sciences, including applications in cancer therapy. Despite having taken two courses in quantum physics at university and working extensively with AI/ML medical devices, I found myself needing to educate myself from scratch on what quantum computing means for our field.

What I discovered was both inspiring and unsettling: we're standing at the edge of a paradigm shift in medical technology, and regulatory science isn't even looking in that direction yet.

What Is Quantum Computing and Why Does It Matter?

Classical computers operate using bits, each either 0 or 1. Quantum computers use qubits that can exist in multiple states simultaneously through superposition. Qubits can also be entangled, meaning their states are interconnected regardless of distance. This allows quantum computers to process vast possibilities in parallel in ways classical computers simply cannot replicate.

For medtech, this isn't academic curiosity—it's transformative capability. Classical computers struggle to simulate even simple molecules beyond a certain size. A molecule with just 30 atoms has more quantum states than a classical computer can practically track.

Quantum computers can model:

  • Drug-molecule interactions at atomic precision

  • Protein folding and three-dimensional structures

  • Tumor microenvironments at cellular and molecular levels

  • Personalized treatment responses based on genetic profiles

The AI-Quantum Hybrid Approach

During our conversation, Sabrina explained: "AI is only as good as the data it's trained on. With quantum computing, we can generate vast amounts of physically accurate data and then train AI on it. Imagine the possibilities from simulating the behavior of ALL atoms in our body."

This is Algorithmiq's innovation: combining quantum computing with AI to address one of AI's fundamental limitations—the need for massive training data.

For many biological phenomena, we simply don't have enough experimental data. It's too expensive, too dangerous, or physically impossible to measure. Quantum computers can generate synthetic training datasets that are physically accurate—based on quantum mechanics—but impossible to obtain experimentally.

Sabrina mentioned that quantum computing capabilities are now available on the cloud, with enterprise access in the million-dollar range annually. For specific computational problems, quantum computers can be more efficient than traditional supercomputers.

Algorithmiq has already announced partnerships with Microsoft (December 2024) and Quantum Circuits (February 2025) to accelerate drug discovery.

A Concrete Example: Photodynamic Cancer Therapy

One particularly compelling application demonstrates quantum computing's real-world impact: photodynamic therapy (PDT) for cancer.

PDT uses special molecules called photosensitizers that are activated by light to produce therapeutic effects. The benefits are significant:

  • No long-term side effects

  • Less invasive than surgery

  • Outpatient procedure

  • Precisely targeted

  • Can be repeated at the same site (unlike radiation)

  • 5-10 times less costly than other cancer treatments

The challenge lies in designing these photosensitizer molecules. It requires understanding tiny energy gaps between electronic states—differences that dictate how molecules behave when exposed to light. Classical quantum chemistry algorithms struggle to calculate these energy gaps with the necessary accuracy.

Using IQM's Emerald quantum processing unit and Algorithmiq's advanced error mitigation techniques, the team achieved a 100x improvement in precision compared to results from other quantum hardware providers. This work, part of the Wellcome Leap Q4Bio Challenge, is establishing an end-to-end quantum-centric drug discovery pipeline for light-activated anti-cancer drugs.

They're focusing on the BODIPY class of compounds—next-generation photosensitizers. With quantum computing, simulating their energy landscape becomes possible with unprecedented accuracy, paving the way for better-targeted therapies developed faster and more cost-effectively.

This is happening now.

Closing Health Data Gaps

We also discussed possibilities that particularly resonate with my work in femtech: using quantum computing to simulate complex biological systems like women's physiology to close health data gaps that are difficult or impossible to obtain experimentally.

Women's health research has historically been underfunded. Menstrual cycles, pregnancy, menopause—these introduce biological complexity that makes clinical trials more expensive and results harder to interpret. What if quantum simulation could help bridge these gaps by modeling hormonal interactions and reproductive system responses with atomic-level precision?

The Regulatory Challenges Ahead

Here's the uncomfortable truth: none of medtech's regulations or guidances currently contemplate quantum-AI hybrid diagnostics or therapeutics.

Challenge 1: The Validation Paradox

How do you validate quantum-generated data when you can't reproduce it classically?

The FDA's recent draft guidance on AI/ML-enabled device software (January 2025) requires manufacturers to disclose synthetic data provenance, describe algorithms used to generate it, and demonstrate it preserves clinical correlations. These are sensible requirements for classically-generated synthetic data.

But they break down when the "algorithm" is a quantum computer simulating physics that classical systems fundamentally cannot reproduce. How do you verify quantum-generated molecular data "preserves clinical correlations" when there's no classical ground truth? The entire point of quantum computing is simulating phenomena classical computers cannot.

Challenge 2: Black Box Squared

AI is already a "black box"—how do we maintain our ability to explain and reproduce the operating principles when we layer quantum computing on top?

Explainability is already a regulatory challenge. The EU MDR Article 61 and FDA guidance emphasize transparency in clinical decision-making. But AI models, particularly deep learning, are notoriously opaque.

Add quantum computing—inherently probabilistic, extraordinarily sensitive to environmental interference—and we're layering one form of opacity on another. Yet regulatory frameworks require that medical devices be explainable, reproducible, and transparent.

The FDA's three-pillar framework for Software as a Medical Device asks:

  1. Is there a valid clinical association between device output and clinical condition?

  2. Does the software correctly process input data?

  3. Does use of the output achieve the intended purpose?

For quantum-AI systems, how do you analytically validate "correctness" when there's no classical benchmark?

Challenge 3: Cybersecurity and Q-Day

Quantum computers will eventually break current encryption methods—a threat called "Q-Day." This poses serious risks:

  • Adversaries can collect encrypted medical data today and decrypt it later

  • Medical devices relying on current cryptographic protocols will be compromised

NIST announced its fifth quantum-safe algorithm in March 2025, but adoption in medical devices has been slow. Medical device manufacturers should implement quantum-resistant encryption immediately.

What Regulatory Frameworks Currently Exist?

The closest we have are two recent FDA guidances:

FDA Guidance on Real-World Evidence (December 2025) emphasizes that data must be relevant and reliable, with a fit-for-purpose approach. This potentially opens a pathway: quantum-generated synthetic data could be acceptable if manufacturers demonstrate it's the most appropriate method for answering specific clinical questions.

FDA Guidance on AI/ML-Enabled Device Software (Draft, January 2025) addresses data management, synthetic data requirements, performance validation—but all assuming classical computational paradigms.

Neither contemplates quantum-generated training data, validation when classical reproduction is impossible, or uncertainty quantification for quantum probabilistic outputs.

The EU AI Act, MDR/IVDR, and ISO standards similarly don't address quantum computing.

Why This Matters Now

Waiting until quantum devices reach the clinic or reach their "ChatGPT moment" means we'll be reactive instead of proactive, again.

We've seen this pattern with AI. By the time ChatGPT brought AI to mainstream awareness, the technology had been developing for decades. Regulators scrambled to catch up.

But AI builds on classical computing principles we already understood. Quantum computing is fundamentally different. The learning curve is steeper, the validation challenges more complex.

If we wait until a quantum-enhanced diagnostic applies for FDA clearance before starting these conversations, we'll be years behind.

So, what needs to happen?

Regulatory agencies may:

  • Introduce quantum-aware terminology in guidance documents

  • Establish working groups bringing together quantum scientists, device developers, and regulatory professionals

  • Develop validation frameworks specifically for quantum-generated synthetic data

  • Issue guidance on quantum-resistant cybersecurity for medical devices

Industry may:

  • Engage early with regulators through pre-submission meetings

  • Document quantum approaches in detail

  • Build quantum literacy within regulatory and quality teams

  • Implement post-quantum cryptography now

Thank you to Sabrina Maniscalco for the thought-provoking conversation, and to Camera di Commercio Italiana a Zurigo for creating the space where these insights happen.

References

Giulia Paggiola
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